






"""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 		snake_case    (    UpperCAmelCase			):
    __magic_name__       	=	['''loss''', '''ce_loss''', '''mlm_loss''', '''hid_loss_enc''', '''hid_loss_dec''']





    def __init__(				self      :   int							,						A      :   List[str]     ):


      '''simple docstring'''




      assert Path(hparams.data_dir     ).exists()
      a	:     Any							=  Path(hparams.output_dir     )
      self.output_dir.mkdir(exist_ok=A     )

      a	:     Optional[int]							=  self.output_dir.joinpath('student'     )

      a	:     Optional[Any]							=  str(A     )  # Tell lightning we are training the student
      a	:     Dict							=  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:
        a	:     List[str]							=  AutoModelForSeqaSeqLM.from_pretrained(hparams.student     )
        use_task_specific_params(A							,						hparams.task     )
        a,	a	:     Dict							=  None, None
      else:
        a,	a,	a	:     Optional[Any]							=  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:
        a	:     Tuple							=  hparams.length_penalty
      a	:     List[Any]							=  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":
        a	:     Optional[Any]							=  len(student.get_encoder().block     )
        a	:     Tuple							=  len(student.get_decoder().block     )
        a	:     Tuple							=  len(teacher.get_encoder().block     )
        a	:     List[str]							=  len(teacher.get_decoder().block     )
      else:
        a	:     int							=  student.config.encoder_layers
        a	:     Dict							=  student.config.decoder_layers
        a	:     Optional[int]							=  teacher.config.encoder_layers
        a	:     Optional[int]							=  teacher.config.decoder_layers

      a	:     Union[str, Any]							=  not (hparams.student is None or hparams.teacher == hparams.student)
      a	:     List[Any]							=  (not self.different_base_models) and hparams.alpha_hid > 0
      a	:     int							=  self.different_base_models or (student_encoder_layers != teacher_encoder_layers)
      # self.different_encoder determines whether we need to run the teacher encoder
      a	:     Optional[int]							=  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	:     Optional[Any]							=  list(range(A     )     )
      if d_layer_ids is None:
        a	:     Optional[Any]							=  list(range(A     )     )

      a,	a	:     List[Any]							=  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	:     Union[str, Any]							=  get_layers_to_supervise(
              n_student=len(self.e_layer_ids     )							,						n_teacher=A     )
          a	:     int							=  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
          a	:     str							=  self.e_layer_ids
          a	:     List[Any]							=  self.d_layer_ids
      else:
        a	:     List[Any]							=  None
        a	:     Any							=  None

      a	:     Optional[Any]							=  nn.KLDivLoss(reduction='batchmean'     )
      a	:     int							=  2.0
      a	:     Tuple							=  hparams.alpha_mlm
      a	:     Any							=  hparams.alpha_ce
      a	:     List[Any]							=  hparams.alpha_hid
      gc.collect()
      torch.cuda.empty_cache()





    def 							lowerCamelCase__ (				self      :   Tuple							,						A      :   int							,						A      :   List[str]							,						A      :   str     ):


      '''simple docstring'''




      a	:     int							=  mask[:, :, None].expand_as(A     )
      a	:     Dict							=  s_logits.size(-1     )
      a	:     Tuple							=  torch.masked_select(A							,						A     )  # (bs * seq_length * voc_size) modulo the 1s in mask
      a	:     List[Any]							=  torch.masked_select(A							,						A     )  # (bs * seq_length * voc_size) modulo the 1s in mask
      a	:     str							=  s_logits_slct.view(-1							,						A     )  # (bs * seq_length, voc_size) modulo the 1s in mask
      a	:     Tuple							=  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()
      a	:     str							=  (
          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 							lowerCamelCase__ (				A      :   List[Any]							,						A      :   Any     ):


      '''simple docstring'''




      SummarizationModule.add_model_specific_args(A							,						A     )
      add_distill_args(A     )
      return parser





    def 							lowerCamelCase__ (				self      :   Tuple							,						A      :   dict     ):


      '''simple docstring'''




      a	:     int							=  self.tokenizer.pad_token_id
      a,	a,	a	:     int							=  batch['input_ids'], batch['attention_mask'], batch['labels']
      if isinstance(self.model							,						A     ):
        a	:     int							=  self.model._shift_right(A     )
      else:
        a	:     Optional[Any]							=  shift_tokens_right(A							,						A     )

      # noinspection PyCallingNonCallable
      a	:     Any							=  self(
          A							,						attention_mask=A							,						decoder_input_ids=A							,						output_hidden_states=self.do_calc_hidden_loss							,						output_attentions=A							,						use_cache=A							,						)
      a	:     Dict							=  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	:     str							=  nn.CrossEntropyLoss(ignore_index=A     )
        a	:     Tuple							=  loss_fct(lm_logits.view(-1							,						lm_logits.shape[-1]     )							,						labels.view(-1     )     )
      else:
        a	:     List[str]							=  nn.functional.log_softmax(A							,						dim=-1     )
        a,	a	:     int							=  label_smoothed_nll_loss(
            A							,						A							,						self.hparams.label_smoothing							,						ignore_index=A     )

      def zero_tensor():
        return torch.tensor(0.0     ).type_as(A     )

      a	:     int							=  student_outputs[
          'encoder_last_hidden_state'
      ]  # use this unless self.different_base_models
      a,	a	:     List[Any]							=  zero_tensor(), zero_tensor()
      if self.different_encoder:  # compute encoder hidden state loss
        a	:     Any							=  self.teacher.get_encoder()(
            A							,						attention_mask=A							,						output_hidden_states=self.do_calc_hidden_loss							,						)
        if self.different_base_models:
          a	:     Dict							=  all_teacher_encoder_outputs['last_hidden_state']
        elif self.do_calc_hidden_loss:
          a	:     str							=  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							,						)

      a	:     List[str]							=  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							,						)
      a	:     Union[str, Any]							=  decoder_input_ids.ne(A     )
      a	:     int							=  self.calc_ce_loss(A							,						A							,						teacher_outputs['logits']     )
      if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
        a	:     Optional[int]							=  self.calc_hidden_loss(
            A							,						student_outputs['decoder_hidden_states']							,						teacher_outputs['decoder_hidden_states']							,						self.d_matches							,						normalize_hidden=self.hparams.normalize_hidden							,						)

      a	:     str							=  (
          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 							lowerCamelCase__ (				A      :   Optional[Any]							,						A      :   str							,						A      :   int							,						A      :   List[Any]							,						A      :   List[str]     ):


      '''simple docstring'''




      a	:     Optional[int]							=  '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}'''
      a	:     Union[str, Any]							=  attention_mask.to(hidden_states[0]     )
      a	:     Dict							=  mask.sum() * hidden_states[0].size(-1     )
      a	:     str							=  torch.stack([hidden_states[i] for i in range(len(A     )     )]     )
      a	:     List[str]							=  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	:     Optional[Any]							=  nn.functional.layer_norm(A							,						student_states.shape[1:]     )
        a	:     Optional[Any]							=  nn.functional.layer_norm(A							,						teacher_states.shape[1:]     )
      a	:     List[Any]							=  nn.functional.mse_loss(A							,						A							,						reduction='none'     )
      a	:     Tuple							=  (mse * mask.unsqueeze(0     ).unsqueeze(-1     )).sum() / valid_count
      return masked_mse




def      snake_case    (A_ :Union[str, Any] ):







  '''simple docstring'''

  parser.add_argument('--teacher'      ,     type=A_ )
  parser.add_argument('--alpha_ce'      ,     default=0.8      ,     type=A_ )
  parser.add_argument('--alpha_mlm'      ,     default=0.2      ,     type=A_ )
  parser.add_argument('--alpha_hid'      ,     default=0.0      ,     type=A_      ,     required=A_ )
  parser.add_argument('--student'      ,     type=A_      ,     required=A_ )
  parser.add_argument('--student_decoder_layers'      ,     default=1_2      ,     type=A_      ,     required=A_ )
  parser.add_argument('--student_encoder_layers'      ,     default=1_2      ,     type=A_      ,     required=A_ )
  parser.add_argument('--no_teacher'      ,     action='store_true'      ,     default=A_ )
  parser.add_argument('--length_penalty'      ,     type=A_      ,     default=-1 )
  parser.add_argument('--supervise_forward'      ,     action='store_true'      ,     default=A_ )
  parser.add_argument('--normalize_hidden'      ,     action='store_true'      ,     default=A_ )






class 		snake_case    (    UpperCAmelCase			):
    __magic_name__       	=	'''translation'''
    __magic_name__       	=	['''bleu''']
    __magic_name__       	=	'''bleu'''





    def __init__(				self      :   List[Any]							,						A      :   Optional[Any]							,						**A      :   Union[str, Any]     ):


      '''simple docstring'''




      super().__init__(A							,						**A     )
      assert hparams.src_lang is not None
      assert hparams.tgt_lang is not None
      a	:     List[str]							=  hparams.src_lang
      a	:     Tuple							=  hparams.tgt_lang
      if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer							,						A     ):
        a	:     str							=  self.tokenizer.lang_code_to_id[hparams.tgt_lang]





    def 							lowerCamelCase__ (				self      :   Union[str, Any]							,						A      :   List[str]							,						A      :   Any     ):


      '''simple docstring'''




      return calculate_bleu(A							,						A     )





    @staticmethod
    def 							lowerCamelCase__ (				A      :   Optional[Any]							,						A      :   Union[str, Any]     ):


      '''simple docstring'''




      TranslationModule.add_model_specific_args(A							,						A     )
      add_distill_args(A     )
      return parser




def      snake_case    (A_ :Tuple ):







  '''simple docstring'''

  if args.no_teacher:
    a	:     Optional[int]							=  TranslationModule if 'translation' in args.task else SummarizationModule
  else:  # DISTILL WITH TEACHER
    a	:     List[str]							=  TranslationDistiller if 'translation' in args.task else SummarizationDistiller
  a	:     str							=  module_cls.__name__
  print(f'''using module {args.setup_cls}''' )
  a	:     List[str]							=  module_cls(A_ )
  return model




def      snake_case    (A_ :List[Any] ):







  '''simple docstring'''

  Path(args.output_dir ).mkdir(exist_ok=A_ )
  check_output_dir(A_      ,     expected_items=3 )

  a	:     Any							=  create_module(A_ )
  return ft_main(A_      ,     model=A_ )


if __name__ == "__main__":
   _UpperCamelCase							:       List[Any]       					=		argparse.ArgumentParser()
   _UpperCamelCase							:       Tuple       					=		pl.Trainer.add_argparse_args(parser)
   _UpperCamelCase							:       List[str]       					=		SummarizationDistiller.add_model_specific_args(parser, os.getcwd())
   _UpperCamelCase							:       List[Any]       					=		parser.parse_args()

   distill_main(args)












"""simple docstring"""


from __future__ import annotations




def      snake_case    (A_ :int ):







  '''simple docstring'''

  assert (
      isinstance(A_      ,     A_ ) and mask > 0
  ), f'''mask needs to be positive integer, your input {mask}'''
  a	:     Any							=  []
  a	:     Union[str, Any]							=  mask

  while submask:
    all_submasks.append(A_ )
    a	:     Any							=  (submask - 1) & mask

  return all_submasks


if __name__ == "__main__":
   import doctest

   doctest.testmod()




