






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        _lowerCAmelCase( UpperCAmelCase_      ):





       """simple docstring"""







       a       :	List[Any]								=['''loss''', '''ce_loss''', '''mlm_loss''', '''hid_loss_enc''', '''hid_loss_dec''']





       def __init__(      self						,    _lowerCamelCase							):
         assert Path(hparams.data_dir							).exists()
         UpperCamelCase_:      List[Any]					   =       Path(hparams.output_dir							)
         self.output_dir.mkdir(exist_ok=_lowerCamelCase							)

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

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

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

         UpperCamelCase_   ,UpperCamelCase_:      str					   =       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:
             UpperCamelCase_:      Any					   =       get_layers_to_supervise(
                 n_student=len(self.e_layer_ids							)						,    n_teacher=_lowerCamelCase							)
             UpperCamelCase_:      Optional[int]					   =       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
             UpperCamelCase_:      Optional[Any]					   =       self.e_layer_ids
             UpperCamelCase_:      str					   =       self.d_layer_ids
         else:
           UpperCamelCase_:      int					   =       None
           UpperCamelCase_:      List[str]					   =       None

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





       def     _a					(      self						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase							):
         UpperCamelCase_:      Any					   =       mask[:, :, None].expand_as(_lowerCamelCase							)
         UpperCamelCase_:      List[str]					   =       s_logits.size(-1							)
         UpperCamelCase_:      str					   =       torch.masked_select(_lowerCamelCase						,    _lowerCamelCase							)  # (bs * seq_length * voc_size) modulo the 1s in mask
         UpperCamelCase_:      Tuple					   =       torch.masked_select(_lowerCamelCase						,    _lowerCamelCase							)  # (bs * seq_length * voc_size) modulo the 1s in mask
         UpperCamelCase_:      int					   =       s_logits_slct.view(-1						,    _lowerCamelCase							)  # (bs * seq_length, voc_size) modulo the 1s in mask
         UpperCamelCase_:      Optional[Any]					   =       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()
         UpperCamelCase_:      List[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     _a					(      _lowerCamelCase						,    _lowerCamelCase							):
         SummarizationModule.add_model_specific_args(_lowerCamelCase						,    _lowerCamelCase							)
         add_distill_args(_lowerCamelCase							)
         return parser





       def     _a					(      self						,    _lowerCamelCase							):
         UpperCamelCase_:      Optional[int]					   =       self.tokenizer.pad_token_id
         UpperCamelCase_   ,UpperCamelCase_   ,UpperCamelCase_:      Dict					   =       batch['input_ids'], batch['attention_mask'], batch['labels']
         if isinstance(self.model						,    _lowerCamelCase							):
           UpperCamelCase_:      str					   =       self.model._shift_right(_lowerCamelCase							)
         else:
           UpperCamelCase_:      Optional[Any]					   =       shift_tokens_right(_lowerCamelCase						,    _lowerCamelCase							)

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

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

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

         UpperCamelCase_:      Tuple					   =       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						,    )
         UpperCamelCase_:      Optional[Any]					   =       decoder_input_ids.ne(_lowerCamelCase							)
         UpperCamelCase_:      Optional[Any]					   =       self.calc_ce_loss(_lowerCamelCase						,    _lowerCamelCase						,    teacher_outputs['logits']							)
         if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
           UpperCamelCase_:      Any					   =       self.calc_hidden_loss(
               _lowerCamelCase						,    student_outputs['decoder_hidden_states']						,    teacher_outputs['decoder_hidden_states']						,    self.d_matches						,    normalize_hidden=self.hparams.normalize_hidden						,    )

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







def 						snake_case      (UpperCAmelCase__						) ->       List[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=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=1_2			,					type=UpperCAmelCase__			,					required=UpperCAmelCase__						)
  parser.add_argument('--student_encoder_layers'			,					default=1_2			,					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        _lowerCAmelCase( UpperCAmelCase_      ):





       """simple docstring"""







       a       :	Dict								='''translation'''
       a       :	List[Any]								=['''bleu''']
       a       :	Dict								='''bleu'''





       def __init__(      self						,    _lowerCamelCase						,    **_lowerCamelCase							):
         super().__init__(_lowerCamelCase						,    **_lowerCamelCase							)
         assert hparams.src_lang is not None
         assert hparams.tgt_lang is not None
         UpperCamelCase_:      Optional[Any]					   =       hparams.src_lang
         UpperCamelCase_:      Optional[int]					   =       hparams.tgt_lang
         if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer						,    _lowerCamelCase							):
           UpperCamelCase_:      str					   =       self.tokenizer.lang_code_to_id[hparams.tgt_lang]





       def     _a					(      self						,    _lowerCamelCase						,    _lowerCamelCase							):
         return calculate_bleu(_lowerCamelCase						,    _lowerCamelCase							)





       @staticmethod
       def     _a					(      _lowerCamelCase						,    _lowerCamelCase							):
         TranslationModule.add_model_specific_args(_lowerCamelCase						,    _lowerCamelCase							)
         add_distill_args(_lowerCamelCase							)
         return parser







def 						snake_case      (UpperCAmelCase__						) ->       List[str]:
  if args.no_teacher:
    UpperCamelCase_:      List[Any]					   =       TranslationModule if 'translation' in args.task else SummarizationModule
  else:  # DISTILL WITH TEACHER
    UpperCamelCase_:      List[str]					   =       TranslationDistiller if 'translation' in args.task else SummarizationDistiller
  UpperCamelCase_:      str					   =       module_cls.__name__
  print(F'''using module {args.setup_cls}'''						)
  UpperCamelCase_:      Union[str, Any]					   =       module_cls(UpperCAmelCase__						)
  return model

def 						snake_case      (UpperCAmelCase__						) ->       Tuple:
  Path(args.output_dir						).mkdir(exist_ok=UpperCAmelCase__						)
  check_output_dir(UpperCAmelCase__			,					expected_items=3						)

  UpperCamelCase_:      Optional[Any]					   =       create_module(UpperCAmelCase__						)
  return ft_main(UpperCAmelCase__			,					model=UpperCAmelCase__						)


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

   distill_main(args)







def 						snake_case      (UpperCAmelCase__ = 4_0_0_0_0_0_0						) ->       int:

  if n <= 1:
    return 0
  UpperCamelCase_:      Dict					   =       0
  UpperCamelCase_:      Union[str, Any]					   =       2
  UpperCamelCase_:      int					   =       0
  while 4 * b + a <= n:
    UpperCamelCase_   ,UpperCamelCase_:      List[str]					   =       b, 4 * b + a
    count += a
  return count + b


if __name__ == "__main__":
   print(F'''{solution() = }''')