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       a_  (       snake_case_ ):
  
      '''simple docstring'''
      UpperCamelCase									=				['''loss''', '''ce_loss''', '''mlm_loss''', '''hid_loss_enc''', '''hid_loss_dec''']
  
  
      def __init__(	self				,	A )	->     List[str]:
          assert Path(hparams.data_dir ).exists()
          _SCREAMING_SNAKE_CASE             =    Path(hparams.output_dir )
          self.output_dir.mkdir(exist_ok=A )
  
          _SCREAMING_SNAKE_CASE             =    self.output_dir.joinpath("""student""" )
  
          _SCREAMING_SNAKE_CASE             =    str(A )  # Tell lightning we are training the student
          _SCREAMING_SNAKE_CASE             =    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:
              _SCREAMING_SNAKE_CASE             =    AutoModelForSeqaSeqLM.from_pretrained(hparams.student )
              use_task_specific_params(A				,	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(
                  A				,	e=hparams.student_encoder_layers				,	d=hparams.student_decoder_layers				,	save_path=A )
  
          if hparams.length_penalty != -1:
              _SCREAMING_SNAKE_CASE             =    hparams.length_penalty
          _SCREAMING_SNAKE_CASE             =    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":
              _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(A ) )
          if d_layer_ids is None:
              _SCREAMING_SNAKE_CASE             =    list(range(A ) )
  
          _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=A )
                  _SCREAMING_SNAKE_CASE             =    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
                  _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				,	A				,	A				,	A )	->     str:
          _SCREAMING_SNAKE_CASE             =    mask[:, :, None].expand_as(A )
          _SCREAMING_SNAKE_CASE             =    s_logits.size(-1 )
          _SCREAMING_SNAKE_CASE             =    torch.masked_select(A				,	A )  # (bs * seq_length * voc_size) modulo the 1s in mask
          _SCREAMING_SNAKE_CASE             =    torch.masked_select(A				,	A )  # (bs * seq_length * voc_size) modulo the 1s in mask
          _SCREAMING_SNAKE_CASE             =    s_logits_slct.view(-1				,	A )  # (bs * seq_length, voc_size) modulo the 1s in mask
          _SCREAMING_SNAKE_CASE             =    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()
          _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_(	A				,	A )	->     Union[str, Any]:
          SummarizationModule.add_model_specific_args(A				,	A )
          add_distill_args(A )
          return parser
  
  
      def     snake_case_(	self				,	A )	->     tuple:
          _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				,	A ):
              _SCREAMING_SNAKE_CASE             =    self.model._shift_right(A )
          else:
              _SCREAMING_SNAKE_CASE             =    shift_tokens_right(A				,	A )
  
          # noinspection PyCallingNonCallable
          _SCREAMING_SNAKE_CASE             =    self(
              A				,	attention_mask=A				,	decoder_input_ids=A				,	output_hidden_states=self.do_calc_hidden_loss				,	output_attentions=A				,	use_cache=A				,	)
          _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=A )
              _SCREAMING_SNAKE_CASE             =    loss_fct(lm_logits.view(-1				,	lm_logits.shape[-1] )				,	labels.view(-1 ) )
          else:
              _SCREAMING_SNAKE_CASE             =    nn.functional.log_softmax(A				,	dim=-1 )
              _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    label_smoothed_nll_loss(
                  A				,	A				,	self.hparams.label_smoothing				,	ignore_index=A )
  
          def zero_tensor():
              return torch.tensor(0.0 ).type_as(A )
  
          _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()(
                  A				,	attention_mask=A				,	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(
                      A				,	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(
              A				,	attention_mask=A				,	encoder_outputs=(teacher_enc_outputs,)				,	decoder_input_ids=A				,	output_hidden_states=self.do_calc_hidden_loss				,	use_cache=A				,	)
          _SCREAMING_SNAKE_CASE             =    decoder_input_ids.ne(A )
          _SCREAMING_SNAKE_CASE             =    self.calc_ce_loss(A				,	A				,	teacher_outputs["""logits"""] )
          if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
              _SCREAMING_SNAKE_CASE             =    self.calc_hidden_loss(
                  A				,	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_(	A				,	A				,	A				,	A				,	A )	->     Tuple:
          _SCREAMING_SNAKE_CASE             =    """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}'
          _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(A ) )] )
          _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(A				,	student_states.shape[1:] )
              _SCREAMING_SNAKE_CASE             =    nn.functional.layer_norm(A				,	teacher_states.shape[1:] )
          _SCREAMING_SNAKE_CASE             =    nn.functional.mse_loss(A				,	A				,	reduction="""none""" )
          _SCREAMING_SNAKE_CASE             =    (mse * mask.unsqueeze(0 ).unsqueeze(-1 )).sum() / valid_count
          return masked_mse
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					Optional[int]					)		->str:
      # NOTE: if --student argument was specified and the teacher and student base models
      # are different, the models still have to have the same tokenizer, specified by
      # --tokenizer_name. So, for example, you can distill from t5_large to t5_small but not
      # from bart to t5. This s because if the tokenizers are different, the output space
      # for the two models is also different and their logits are not comparable.
      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       a_  (       snake_case_ ):
  
      '''simple docstring'''
      UpperCamelCase									=				'''translation'''
      UpperCamelCase									=				['''bleu''']
      UpperCamelCase									=				'''bleu'''
  
  
      def __init__(	self				,	A				,	**A )	->     Any:
          super().__init__(A				,	**A )
          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				,	A ):
              _SCREAMING_SNAKE_CASE             =    self.tokenizer.lang_code_to_id[hparams.tgt_lang]
  
  
      def     snake_case_(	self				,	A				,	A )	->     dict:
          return calculate_bleu(A				,	A )
  
  
  
  
  
      @staticmethod
      def     snake_case_(	A				,	A )	->     Any:
          TranslationModule.add_model_specific_args(A				,	A )
          add_distill_args(A )
          return parser
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					Union[str, Any]					)		->Tuple:
      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(__lowerCamelCase					)
      return model
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					List[str]					)		->str:
      Path(args.output_dir					).mkdir(exist_ok=__lowerCamelCase					)
      check_output_dir(__lowerCamelCase      , expected_items=3					)
  
      _SCREAMING_SNAKE_CASE             =    create_module(__lowerCamelCase					)
      return ft_main(__lowerCamelCase      , model=__lowerCamelCase					)
  
  
  if __name__ == "__main__":
        lowercase_			       =				argparse.ArgumentParser()
        lowercase_			       =				pl.Trainer.add_argparse_args(parser)
        lowercase_			       =				SummarizationDistiller.add_model_specific_args(parser, os.getcwd())
        lowercase_			       =				parser.parse_args()
  
        distill_main(args)
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  import json
  import os
  import tempfile
  import unittest
  
  from transformers.modelcard import ModelCard
  
  
  class       a_  (       unittest.TestCase ):
  
      '''simple docstring'''
  
  
      def     snake_case_(	self )	->     int:
          _SCREAMING_SNAKE_CASE             =    {
              """model_details""": {
                  """Organization""": """testing""",
                  """Model date""": """today""",
                  """Model version""": """v2.1, Developed by Test Corp in 2019.""",
                  """Architecture""": """Convolutional Neural Network.""",
              },
              """metrics""": """BLEU and ROUGE-1""",
              """evaluation_data""": {
                  """Datasets""": {"""BLEU""": """My-great-dataset-v1""", """ROUGE-1""": """My-short-dataset-v2.1"""},
                  """Preprocessing""": """See details on https://arxiv.org/pdf/1810.03993.pdf""",
              },
              """training_data""": {
                  """Dataset""": """English Wikipedia dump dated 2018-12-01""",
                  """Preprocessing""": (
                      """Using SentencePiece vocabulary of size 52k tokens. See details on"""
                      """ https://arxiv.org/pdf/1810.03993.pdf"""
                  ),
              },
              """quantitative_analyses""": {"""BLEU""": 55.1, """ROUGE-1""": 76},
          }
  
  
      def     snake_case_(	self )	->     Tuple:
          _SCREAMING_SNAKE_CASE             =    ModelCard.from_dict(self.inputs_dict )
          self.assertTrue(hasattr(A				,	"""model_details""" ) )
          self.assertTrue(hasattr(A				,	"""intended_use""" ) )
          self.assertTrue(hasattr(A				,	"""factors""" ) )
          self.assertTrue(hasattr(A				,	"""metrics""" ) )
          self.assertTrue(hasattr(A				,	"""evaluation_data""" ) )
          self.assertTrue(hasattr(A				,	"""training_data""" ) )
          self.assertTrue(hasattr(A				,	"""quantitative_analyses""" ) )
          self.assertTrue(hasattr(A				,	"""ethical_considerations""" ) )
          self.assertTrue(hasattr(A				,	"""caveats_and_recommendations""" ) )
  
  
      def     snake_case_(	self )	->     List[str]:
          _SCREAMING_SNAKE_CASE             =    ModelCard.from_dict(self.inputs_dict )
          _SCREAMING_SNAKE_CASE             =    json.loads(modelcard.to_json_string() )
          for key, value in self.inputs_dict.items():
              self.assertEqual(obj[key]				,	A )
  
  
      def     snake_case_(	self )	->     Any:
          _SCREAMING_SNAKE_CASE             =    ModelCard.from_dict(self.inputs_dict )
  
          with tempfile.TemporaryDirectory() as tmpdirname:
              _SCREAMING_SNAKE_CASE             =    os.path.join(A				,	"""modelcard.json""" )
              model_card_first.to_json_file(A )
              _SCREAMING_SNAKE_CASE             =    ModelCard.from_json_file(A )
  
          self.assertEqual(model_card_second.to_dict()				,	model_card_first.to_dict() )
  
  
  
  
  
      def     snake_case_(	self )	->     List[Any]:
          _SCREAMING_SNAKE_CASE             =    ModelCard.from_dict(self.inputs_dict )
  
          with tempfile.TemporaryDirectory() as tmpdirname:
              model_card_first.save_pretrained(A )
              _SCREAMING_SNAKE_CASE             =    ModelCard.from_pretrained(A )
  
          self.assertEqual(model_card_second.to_dict()				,	model_card_first.to_dict() )
  
  
  
  