code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  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 							__UpperCAmelCase  (       A__						):
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		__lowerCAmelCase 				=    ['''loss''', '''ce_loss''', '''mlm_loss''', '''hid_loss_enc''', '''hid_loss_dec''']
  
  
  
  
  
  
  
  		def __init__(self						:      List[str]      ,      _lowerCAmelCase						:      Union[str, Any]      ):
  			assert Path(hparams.data_dir      ).exists()
  			A							  =      Path(hparams.output_dir      )
  			self.output_dir.mkdir(exist_ok=_lowerCAmelCase      )
  
  			A							  =      self.output_dir.joinpath("""student"""      )
  
  			A							  =      str(_lowerCAmelCase      )  # Tell lightning we are training the student
  			A							  =      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:
  				A							  =      AutoModelForSeqaSeqLM.from_pretrained(hparams.student      )
  				use_task_specific_params(_lowerCAmelCase      ,      hparams.task      )
  				A  ,    A							  =      None, None
  			else:
  				A  ,    A  ,    A							  =      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:
  				A							  =      hparams.length_penalty
  			A							  =      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":
  				A							  =      len(student.get_encoder().block      )
  				A							  =      len(student.get_decoder().block      )
  				A							  =      len(teacher.get_encoder().block      )
  				A							  =      len(teacher.get_decoder().block      )
  			else:
  				A							  =      student.config.encoder_layers
  				A							  =      student.config.decoder_layers
  				A							  =      teacher.config.encoder_layers
  				A							  =      teacher.config.decoder_layers
  
  			A							  =      not (hparams.student is None or hparams.teacher == hparams.student)
  			A							  =      (not self.different_base_models) and hparams.alpha_hid > 0
  			A							  =      self.different_base_models or (student_encoder_layers != teacher_encoder_layers)
  			# self.different_encoder determines whether we need to run the teacher encoder
  			A							  =      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:
  				A							  =      list(range(_lowerCAmelCase      )      )
  			if d_layer_ids is None:
  				A							  =      list(range(_lowerCAmelCase      )      )
  
  			A  ,    A							  =      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:
  					A							  =      get_layers_to_supervise(
  					    n_student=len(self.e_layer_ids      )      ,      n_teacher=_lowerCAmelCase      )
  					A							  =      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
  					A							  =      self.e_layer_ids
  					A							  =      self.d_layer_ids
  			else:
  				A							  =      None
  				A							  =      None
  
  			A							  =      nn.KLDivLoss(reduction="""batchmean"""      )
  			A							  =      2.0
  			A							  =      hparams.alpha_mlm
  			A							  =      hparams.alpha_ce
  			A							  =      hparams.alpha_hid
  			gc.collect()
  			torch.cuda.empty_cache()
  
  
  
  
  
  
  
  		def        A     (self						:      List[str]      ,      _lowerCAmelCase						:      int      ,      _lowerCAmelCase						:      List[str]      ,      _lowerCAmelCase						:      List[Any]      ):
  			A							  =      mask[:, :, None].expand_as(_lowerCAmelCase      )
  			A							  =      s_logits.size(-1      )
  			A							  =      torch.masked_select(_lowerCAmelCase      ,      _lowerCAmelCase      )  # (bs * seq_length * voc_size) modulo the 1s in mask
  			A							  =      torch.masked_select(_lowerCAmelCase      ,      _lowerCAmelCase      )  # (bs * seq_length * voc_size) modulo the 1s in mask
  			A							  =      s_logits_slct.view(-1      ,      _lowerCAmelCase      )  # (bs * seq_length, voc_size) modulo the 1s in mask
  			A							  =      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()
  			A							  =      (
  			    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						:      Optional[int]      ,      _lowerCAmelCase						:      int      ):
  			SummarizationModule.add_model_specific_args(_lowerCAmelCase      ,      _lowerCAmelCase      )
  			add_distill_args(_lowerCAmelCase      )
  			return parser
  
  
  
  
  
  
  
  		def        A     (self						:      List[str]      ,      _lowerCAmelCase						:      dict      ):
  			A							  =      self.tokenizer.pad_token_id
  			A  ,    A  ,    A							  =      batch["""input_ids"""], batch["""attention_mask"""], batch["""labels"""]
  			if isinstance(self.model      ,      _lowerCAmelCase      ):
  				A							  =      self.model._shift_right(_lowerCAmelCase      )
  			else:
  				A							  =      shift_tokens_right(_lowerCAmelCase      ,      _lowerCAmelCase      )
  
  			# noinspection PyCallingNonCallable
  			A							  =      self(
  			    _lowerCAmelCase      ,      attention_mask=_lowerCAmelCase      ,      decoder_input_ids=_lowerCAmelCase      ,      output_hidden_states=self.do_calc_hidden_loss      ,      output_attentions=_lowerCAmelCase      ,      use_cache=_lowerCAmelCase      ,      )
  			A							  =      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
  				A							  =      nn.CrossEntropyLoss(ignore_index=_lowerCAmelCase      )
  				A							  =      loss_fct(lm_logits.view(-1      ,      lm_logits.shape[-1]      )      ,      labels.view(-1      )      )
  			else:
  				A							  =      nn.functional.log_softmax(_lowerCAmelCase      ,      dim=-1      )
  				A  ,    A							  =      label_smoothed_nll_loss(
  				    _lowerCAmelCase      ,      _lowerCAmelCase      ,      self.hparams.label_smoothing      ,      ignore_index=_lowerCAmelCase      )
  
  			def zero_tensor():
  				return torch.tensor(0.0      ).type_as(_lowerCAmelCase      )
  
  			A							  =      student_outputs[
  			    """encoder_last_hidden_state"""
  			]  # use this unless self.different_base_models
  			A  ,    A							  =      zero_tensor(), zero_tensor()
  			if self.different_encoder:  # compute encoder hidden state loss
  				A							  =      self.teacher.get_encoder()(
  				    _lowerCAmelCase      ,      attention_mask=_lowerCAmelCase      ,      output_hidden_states=self.do_calc_hidden_loss      ,      )
  				if self.different_base_models:
  					A							  =      all_teacher_encoder_outputs["""last_hidden_state"""]
  				elif self.do_calc_hidden_loss:
  					A							  =      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      ,      )
  
  			A							  =      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      ,      )
  			A							  =      decoder_input_ids.ne(_lowerCAmelCase      )
  			A							  =      self.calc_ce_loss(_lowerCAmelCase      ,      _lowerCAmelCase      ,      teacher_outputs["""logits"""]      )
  			if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
  				A							  =      self.calc_hidden_loss(
  				    _lowerCAmelCase      ,      student_outputs["""decoder_hidden_states"""]      ,      teacher_outputs["""decoder_hidden_states"""]      ,      self.d_matches      ,      normalize_hidden=self.hparams.normalize_hidden      ,      )
  
  			A							  =      (
  			    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						:      Optional[int]      ,      _lowerCAmelCase						:      int      ,      _lowerCAmelCase						:      List[str]      ,      _lowerCAmelCase						:      int      ,      _lowerCAmelCase						:      Any      ):
  			A							  =      """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}"""
  			A							  =      attention_mask.to(hidden_states[0]      )
  			A							  =      mask.sum() * hidden_states[0].size(-1      )
  			A							  =      torch.stack([hidden_states[i] for i in range(len(_lowerCAmelCase      )      )]      )
  			A							  =      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:
  				A							  =      nn.functional.layer_norm(_lowerCAmelCase      ,      student_states.shape[1:]      )
  				A							  =      nn.functional.layer_norm(_lowerCAmelCase      ,      teacher_states.shape[1:]      )
  			A							  =      nn.functional.mse_loss(_lowerCAmelCase      ,      _lowerCAmelCase      ,      reduction="""none"""      )
  			A							  =      (mse * mask.unsqueeze(0      ).unsqueeze(-1      )).sum() / valid_count
  			return masked_mse
  
  
  
  
  
  
  def    __a (     UpperCAmelCase    )      ->Optional[int]:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	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=12       ,			type=UpperCAmelCase       ,			required=UpperCAmelCase    )
  	parser.add_argument("""--student_encoder_layers"""       ,			default=12       ,			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 							__UpperCAmelCase  (       A__						):
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		__lowerCAmelCase 				=    '''translation'''
  		__lowerCAmelCase 				=    ['''bleu''']
  		__lowerCAmelCase 				=    '''bleu'''
  
  
  
  
  
  
  
  		def __init__(self						:      Tuple      ,      _lowerCAmelCase						:      Dict      ,      **_lowerCAmelCase						:      Dict      ):
  			super().__init__(_lowerCAmelCase      ,      **_lowerCAmelCase      )
  			assert hparams.src_lang is not None
  			assert hparams.tgt_lang is not None
  			A							  =      hparams.src_lang
  			A							  =      hparams.tgt_lang
  			if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer      ,      _lowerCAmelCase      ):
  				A							  =      self.tokenizer.lang_code_to_id[hparams.tgt_lang]
  
  
  
  
  
  
  
  		def        A     (self						:      List[str]      ,      _lowerCAmelCase						:      List[Any]      ,      _lowerCAmelCase						:      List[Any]      ):
  			return calculate_bleu(_lowerCAmelCase      ,      _lowerCAmelCase      )
  
  
  
  
  
  
  
  		@staticmethod
  		def        A     (_lowerCAmelCase						:      int      ,      _lowerCAmelCase						:      Any      ):
  			TranslationModule.add_model_specific_args(_lowerCAmelCase      ,      _lowerCAmelCase      )
  			add_distill_args(_lowerCAmelCase      )
  			return parser
  
  
  
  
  
  
  def    __a (     UpperCAmelCase    )      ->Any:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	if args.no_teacher:
  		A							  =      TranslationModule if """translation""" in args.task else SummarizationModule
  	else:  # DISTILL WITH TEACHER
  		A							  =      TranslationDistiller if """translation""" in args.task else SummarizationDistiller
  	A							  =      module_cls.__name__
  	print(f"""using module {args.setup_cls}"""    )
  	A							  =      module_cls(UpperCAmelCase    )
  	return model
  
  
  
  
  def    __a (     UpperCAmelCase    )      ->Union[str, Any]:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	Path(args.output_dir    ).mkdir(exist_ok=UpperCAmelCase    )
  	check_output_dir(UpperCAmelCase       ,			expected_items=3    )
  
  	A							  =      create_module(UpperCAmelCase    )
  	return ft_main(UpperCAmelCase       ,			model=UpperCAmelCase    )
  
  
  if __name__ == "__main__":
  				_lowerCamelCase				:	Any				    =       argparse.ArgumentParser()
  				_lowerCamelCase				:	Union[str, Any]				    =       pl.Trainer.add_argparse_args(parser)
  				_lowerCamelCase				:	Union[str, Any]				    =       SummarizationDistiller.add_model_specific_args(parser, os.getcwd())
  				_lowerCamelCase				:	str				    =       parser.parse_args()
  
  				distill_main(args)
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  from __future__ import annotations
  
  
  
  class 							__UpperCAmelCase  :
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def __init__(self						:      Any      ,      _lowerCAmelCase						:      int      ):
  			A							  =      value
  			A							  =      None
  			A							  =      None
  
  
  
  
  
  
  class 							__UpperCAmelCase  :
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		__lowerCAmelCase 				=    42
  
  
  
  
  
  
  
  		def __init__(self						:      List[str]      ):
  			A							  =      0
  
  
  
  
  
  
  
  		def        A     (self						:      Dict      ,      _lowerCAmelCase						:      Node | None      ,      _lowerCAmelCase						:      int      ):
  			if node is None:
  				return
  
  			if path_sum == self.target:
  				self.paths += 1
  
  			if node.left:
  				self.depth_first_search(node.left      ,      path_sum + node.left.value      )
  			if node.right:
  				self.depth_first_search(node.right      ,      path_sum + node.right.value      )
  
  
  
  
  
  
  
  		def        A     (self						:      Optional[Any]      ,      _lowerCAmelCase						:      Node | None      ,      _lowerCAmelCase						:      int | None = None      ):
  			if node is None:
  				return 0
  			if target is not None:
  				A							  =      target
  
  			self.depth_first_search(_lowerCAmelCase      ,      node.value      )
  			self.path_sum(node.left      )
  			self.path_sum(node.right      )
  
  			return self.paths
  
  
  if __name__ == "__main__":
  				import doctest
  
  				doctest.testmod()
  
  