

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__							( UpperCAmelCase							):







  """simple docstring"""




  UpperCAmelCase__   :			Any      							=["""loss""", """ce_loss""", """mlm_loss""", """hid_loss_enc""", """hid_loss_dec"""]


  def __init__(      self     :			Union[str, Any]					,					UpperCAmelCase__     :			Tuple		)       ->Optional[Any]:
        """simple docstring"""







        assert Path(hparams.data_dir		).exists()
        SCREAMING_SNAKE_CASE			:   Dict	  = Path(hparams.output_dir		)
        self.output_dir.mkdir(exist_ok=UpperCAmelCase__		)

        SCREAMING_SNAKE_CASE			:   Optional[Any]	  = self.output_dir.joinpath("""student"""		)

        SCREAMING_SNAKE_CASE			:   List[Any]	  = str(UpperCAmelCase__		)  # Tell lightning we are training the student
        SCREAMING_SNAKE_CASE			:   Optional[Any]	  = AutoModelForSeqaSeqLM.from_pretrained(hparams.teacher		).eval()
        use_task_specific_params(UpperCAmelCase__					,					hparams.task		)  # We copy good generation parameters to student by default
        if hparams.student is not None:
              SCREAMING_SNAKE_CASE			:   Optional[int]	  = AutoModelForSeqaSeqLM.from_pretrained(hparams.student		)
              use_task_specific_params(UpperCAmelCase__					,					hparams.task		)
              SCREAMING_SNAKE_CASE  ,						SCREAMING_SNAKE_CASE			:   Any	  = None, None
        else:
              SCREAMING_SNAKE_CASE  ,						SCREAMING_SNAKE_CASE  ,						SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = create_student_by_copying_alternating_layers(
                  UpperCAmelCase__					,					e=hparams.student_encoder_layers					,					d=hparams.student_decoder_layers					,					save_path=UpperCAmelCase__		)

        if hparams.length_penalty != -1:
              SCREAMING_SNAKE_CASE			:   Dict	  = hparams.length_penalty
        SCREAMING_SNAKE_CASE			:   Tuple	  = hparams.teacher  # Use teacher's tokenizer
        super().__init__(UpperCAmelCase__					,					model=UpperCAmelCase__					,					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			:   Optional[int]	  = len(student.get_encoder().block		)
              SCREAMING_SNAKE_CASE			:   Optional[Any]	  = len(student.get_decoder().block		)
              SCREAMING_SNAKE_CASE			:   Optional[Any]	  = len(teacher.get_encoder().block		)
              SCREAMING_SNAKE_CASE			:   Dict	  = len(teacher.get_decoder().block		)
        else:
              SCREAMING_SNAKE_CASE			:   Optional[int]	  = student.config.encoder_layers
              SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = student.config.decoder_layers
              SCREAMING_SNAKE_CASE			:   int	  = teacher.config.encoder_layers
              SCREAMING_SNAKE_CASE			:   Any	  = teacher.config.decoder_layers

        SCREAMING_SNAKE_CASE			:   Optional[Any]	  = not (hparams.student is None or hparams.teacher == hparams.student)
        SCREAMING_SNAKE_CASE			:   Tuple	  = (not self.different_base_models) and hparams.alpha_hid > 0
        SCREAMING_SNAKE_CASE			:   Any	  = 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			:   Dict	  = 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			:   Any	  = list(range(UpperCAmelCase__		)		)
        if d_layer_ids is None:
              SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = list(range(UpperCAmelCase__		)		)

        SCREAMING_SNAKE_CASE  ,						SCREAMING_SNAKE_CASE			:   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:
                    SCREAMING_SNAKE_CASE			:   Dict	  = get_layers_to_supervise(
                        n_student=len(self.e_layer_ids		)					,					n_teacher=UpperCAmelCase__		)
                    SCREAMING_SNAKE_CASE			:   Optional[Any]	  = get_layers_to_supervise(
                        n_student=len(self.d_layer_ids		)					,					n_teacher=UpperCAmelCase__		)
              else:  # student layer should emulate hidden states of the teacher layer it was copied from
                    SCREAMING_SNAKE_CASE			:   Any	  = self.e_layer_ids
                    SCREAMING_SNAKE_CASE			:   Optional[Any]	  = self.d_layer_ids
        else:
              SCREAMING_SNAKE_CASE			:   Tuple	  = None
              SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = None

        SCREAMING_SNAKE_CASE			:   int	  = nn.KLDivLoss(reduction="""batchmean"""		)
        SCREAMING_SNAKE_CASE			:   int	  = 2.0
        SCREAMING_SNAKE_CASE			:   str	  = hparams.alpha_mlm
        SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = hparams.alpha_ce
        SCREAMING_SNAKE_CASE			:   Any	  = hparams.alpha_hid
        gc.collect()
        torch.cuda.empty_cache()


  def 			_lowercase   (      self     :			Any					,					UpperCAmelCase__     :			Tuple					,					UpperCAmelCase__     :			Tuple					,					UpperCAmelCase__     :			Tuple		)       ->Any:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   Tuple	  = mask[:, :, None].expand_as(UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE			:   Optional[int]	  = s_logits.size(-1		)
        SCREAMING_SNAKE_CASE			:   Dict	  = torch.masked_select(UpperCAmelCase__					,					UpperCAmelCase__		)  # (bs * seq_length * voc_size) modulo the 1s in mask
        SCREAMING_SNAKE_CASE			:   Optional[Any]	  = torch.masked_select(UpperCAmelCase__					,					UpperCAmelCase__		)  # (bs * seq_length * voc_size) modulo the 1s in mask
        SCREAMING_SNAKE_CASE			:   Dict	  = s_logits_slct.view(-1					,					UpperCAmelCase__		)  # (bs * seq_length, voc_size) modulo the 1s in mask
        SCREAMING_SNAKE_CASE			:   List[str]	  = t_logits_slct.view(-1					,					UpperCAmelCase__		)  # (bs * seq_length, voc_size) modulo the 1s in mask
        assert t_logits_slct.size() == s_logits_slct.size()
        SCREAMING_SNAKE_CASE			:   Optional[Any]	  = (
            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 			_lowercase   (      UpperCAmelCase__     :			List[Any]					,					UpperCAmelCase__     :			Any		)       ->List[Any]:
        """simple docstring"""







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


  def 			_lowercase   (      self     :			Union[str, Any]					,					UpperCAmelCase__     :			dict		)       ->tuple:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   Any	  = self.tokenizer.pad_token_id
        SCREAMING_SNAKE_CASE  ,						SCREAMING_SNAKE_CASE  ,						SCREAMING_SNAKE_CASE			:   Optional[Any]	  = batch["""input_ids"""], batch["""attention_mask"""], batch["""labels"""]
        if isinstance(self.model					,					UpperCAmelCase__		):
              SCREAMING_SNAKE_CASE			:   Optional[int]	  = self.model._shift_right(UpperCAmelCase__		)
        else:
              SCREAMING_SNAKE_CASE			:   Tuple	  = shift_tokens_right(UpperCAmelCase__					,					UpperCAmelCase__		)

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

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

        SCREAMING_SNAKE_CASE			:   str	  = student_outputs[
            """encoder_last_hidden_state"""
        ]  # use this unless self.different_base_models
        SCREAMING_SNAKE_CASE  ,						SCREAMING_SNAKE_CASE			:   Dict	  = zero_tensor(), zero_tensor()
        if self.different_encoder:  # compute encoder hidden state loss
              SCREAMING_SNAKE_CASE			:   str	  = self.teacher.get_encoder()(
                  UpperCAmelCase__					,					attention_mask=UpperCAmelCase__					,					output_hidden_states=self.do_calc_hidden_loss					,					)
              if self.different_base_models:
                    SCREAMING_SNAKE_CASE			:   Dict	  = all_teacher_encoder_outputs["""last_hidden_state"""]
              elif self.do_calc_hidden_loss:
                    SCREAMING_SNAKE_CASE			:   Optional[Any]	  = self.calc_hidden_loss(
                        UpperCAmelCase__					,					student_outputs["""encoder_hidden_states"""]					,					all_teacher_encoder_outputs["""hidden_states"""]					,					self.e_matches					,					normalize_hidden=self.hparams.normalize_hidden					,					)

        SCREAMING_SNAKE_CASE			:   int	  = self.teacher(
            UpperCAmelCase__					,					attention_mask=UpperCAmelCase__					,					encoder_outputs=(teacher_enc_outputs,)					,					decoder_input_ids=UpperCAmelCase__					,					output_hidden_states=self.do_calc_hidden_loss					,					use_cache=UpperCAmelCase__					,					)
        SCREAMING_SNAKE_CASE			:   Any	  = decoder_input_ids.ne(UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE			:   List[Any]	  = self.calc_ce_loss(UpperCAmelCase__					,					UpperCAmelCase__					,					teacher_outputs["""logits"""]		)
        if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
              SCREAMING_SNAKE_CASE			:   List[str]	  = self.calc_hidden_loss(
                  UpperCAmelCase__					,					student_outputs["""decoder_hidden_states"""]					,					teacher_outputs["""decoder_hidden_states"""]					,					self.d_matches					,					normalize_hidden=self.hparams.normalize_hidden					,					)

        SCREAMING_SNAKE_CASE			:   Dict	  = (
            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 			_lowercase   (      UpperCAmelCase__     :			str					,					UpperCAmelCase__     :			Dict					,					UpperCAmelCase__     :			Optional[int]					,					UpperCAmelCase__     :			int					,					UpperCAmelCase__     :			Tuple		)       ->int:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   str	  = """expected list or tuple for hidden_states, got tensor of shape: """
        assert not isinstance(UpperCAmelCase__					,					torch.Tensor		), f"{msg}{hidden_states.shape}"
        assert not isinstance(UpperCAmelCase__					,					torch.Tensor		), f"{msg}{hidden_states_T.shape}"
        SCREAMING_SNAKE_CASE			:   Any	  = attention_mask.to(hidden_states[0]		)
        SCREAMING_SNAKE_CASE			:   Tuple	  = mask.sum() * hidden_states[0].size(-1		)
        SCREAMING_SNAKE_CASE			:   str	  = torch.stack([hidden_states[i] for i in range(len(UpperCAmelCase__		)		)]		)
        SCREAMING_SNAKE_CASE			:   Tuple	  = 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			:   str	  = nn.functional.layer_norm(UpperCAmelCase__					,					student_states.shape[1:]		)
              SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = nn.functional.layer_norm(UpperCAmelCase__					,					teacher_states.shape[1:]		)
        SCREAMING_SNAKE_CASE			:   List[str]	  = nn.functional.mse_loss(UpperCAmelCase__					,					UpperCAmelCase__					,					reduction="""none"""		)
        SCREAMING_SNAKE_CASE			:   List[str]	  = (mse * mask.unsqueeze(0		).unsqueeze(-1		)).sum() / valid_count
        return masked_mse






def        __lowercase   (  _A      )  ->    Any:
      # 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=_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=12    ,     type=_A    ,     required=_A      )
      parser.add_argument("""--student_encoder_layers"""    ,     default=12    ,     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 							a__							( UpperCAmelCase							):







  """simple docstring"""




  UpperCAmelCase__   :			Optional[Any]      							="""translation"""
  UpperCAmelCase__   :			Dict      							=["""bleu"""]
  UpperCAmelCase__   :			Union[str, Any]      							="""bleu"""


  def __init__(      self     :			Optional[int]					,					UpperCAmelCase__     :			Any					,					**UpperCAmelCase__     :			List[Any]		)       ->Optional[Any]:
        """simple docstring"""







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


  def 			_lowercase   (      self     :			Any					,					UpperCAmelCase__     :			Dict					,					UpperCAmelCase__     :			Tuple		)       ->dict:
        """simple docstring"""







        return calculate_bleu(UpperCAmelCase__					,					UpperCAmelCase__		)




  @staticmethod
  def 			_lowercase   (      UpperCAmelCase__     :			Union[str, Any]					,					UpperCAmelCase__     :			Union[str, Any]		)       ->str:
        """simple docstring"""







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






def        __lowercase   (  _A      )  ->    int:
      if args.no_teacher:
            SCREAMING_SNAKE_CASE			:   Dict	  = TranslationModule if """translation""" in args.task else SummarizationModule
      else:  # DISTILL WITH TEACHER
            SCREAMING_SNAKE_CASE			:   List[str]	  = TranslationDistiller if """translation""" in args.task else SummarizationDistiller
      SCREAMING_SNAKE_CASE			:   str	  = module_cls.__name__
      print(F"using module {args.setup_cls}"      )
      SCREAMING_SNAKE_CASE			:   Optional[Any]	  = module_cls(_A      )
      return model






def        __lowercase   (  _A      )  ->    Optional[int]:
      Path(args.output_dir      ).mkdir(exist_ok=_A      )
      check_output_dir(_A    ,     expected_items=3      )

      SCREAMING_SNAKE_CASE			:   int	  = create_module(_A      )
      return ft_main(_A    ,     model=_A      )


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

  distill_main(args)







import itertools






def        __lowercase   (  _A = 1000000      )  ->    int:
      SCREAMING_SNAKE_CASE			:   Tuple	  = [1]

      for i in itertools.count(len(_A      )      ):
            SCREAMING_SNAKE_CASE			:   Optional[int]	  = 0
            for j in itertools.count(1      ):
                  SCREAMING_SNAKE_CASE			:   str	  = -1 if j % 2 == 0 else +1
                  SCREAMING_SNAKE_CASE			:   Any	  = (j * j * 3 - j) // 2
                  if index > i:
                        break
                  item += partitions[i - index] * sign
                  item %= number
                  index += j
                  if index > i:
                        break
                  item += partitions[i - index] * sign
                  item %= number

            if item == 0:
                  return i
            partitions.append(_A      )

      return 0


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  print(F"""{solution() = }""")




