




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							(				lowerCAmelCase):
       _a			       =    ['''loss''', '''ce_loss''', '''mlm_loss''', '''hid_loss_enc''', '''hid_loss_dec''']






       def __init__( self:       int							,       _lowerCAmelCase:       Tuple					):
         assert Path(hparams.data_dir					).exists()
         lowercase     :int   						=    Path(hparams.output_dir					)
         self.output_dir.mkdir(exist_ok=_lowerCAmelCase					)

         lowercase     :List[str]   						=    self.output_dir.joinpath("student"					)

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

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

         lowercase						,     lowercase     :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:
             lowercase     :Tuple   						=    get_layers_to_supervise(
                 n_student=len(self.e_layer_ids					)							,       n_teacher=_lowerCAmelCase					)
             lowercase     :Dict   						=    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
             lowercase     :List[str]   						=    self.e_layer_ids
             lowercase     :str   						=    self.d_layer_ids
         else:
           lowercase     :Union[str, Any]   						=    None
           lowercase     :Tuple   						=    None

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






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






       def        SCREAMING_SNAKE_CASE     ( self:       Tuple							,       _lowerCAmelCase:       dict					):
         lowercase     :Any   						=    self.tokenizer.pad_token_id
         lowercase						,     lowercase						,     lowercase     :Optional[Any]   						=    batch["input_ids"], batch["attention_mask"], batch["labels"]
         if isinstance(self.model							,       _lowerCAmelCase					):
           lowercase     :List[str]   						=    self.model._shift_right(_lowerCAmelCase					)
         else:
           lowercase     :Optional[int]   						=    shift_tokens_right(_lowerCAmelCase							,       _lowerCAmelCase					)

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

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

         lowercase     :Dict   						=    student_outputs[
             "encoder_last_hidden_state"
         ]  # use this unless self.different_base_models
         lowercase						,     lowercase     :str   						=    zero_tensor(), zero_tensor()
         if self.different_encoder:  # compute encoder hidden state loss
           lowercase     :Tuple   						=    self.teacher.get_encoder()(
               _lowerCAmelCase							,       attention_mask=_lowerCAmelCase							,       output_hidden_states=self.do_calc_hidden_loss							,       )
           if self.different_base_models:
             lowercase     :Optional[int]   						=    all_teacher_encoder_outputs["last_hidden_state"]
           elif self.do_calc_hidden_loss:
             lowercase     :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							,       )

         lowercase     :Optional[Any]   						=    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							,       )
         lowercase     :int   						=    decoder_input_ids.ne(_lowerCAmelCase					)
         lowercase     :str   						=    self.calc_ce_loss(_lowerCAmelCase							,       _lowerCAmelCase							,       teacher_outputs["logits"]					)
         if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
           lowercase     :Dict   						=    self.calc_hidden_loss(
               _lowerCAmelCase							,       student_outputs["decoder_hidden_states"]							,       teacher_outputs["decoder_hidden_states"]							,       self.d_matches							,       normalize_hidden=self.hparams.normalize_hidden							,       )

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







def    UpperCAmelCase__	(  lowerCamelCase ):
  # 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  __lowerCAmelCase							(				lowerCAmelCase):
       _a			       =    '''translation'''
       _a			       =    ['''bleu''']
       _a			       =    '''bleu'''






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






       def        SCREAMING_SNAKE_CASE     ( self:       Any							,       _lowerCAmelCase:       str							,       _lowerCAmelCase:       Optional[Any]					):
         return calculate_bleu(_lowerCAmelCase							,       _lowerCAmelCase					)






       @staticmethod
       def        SCREAMING_SNAKE_CASE     ( _lowerCAmelCase:       Union[str, Any]							,       _lowerCAmelCase:       str					):
         TranslationModule.add_model_specific_args(_lowerCAmelCase							,       _lowerCAmelCase					)
         add_distill_args(_lowerCAmelCase					)
         return parser







def    UpperCAmelCase__	(  lowerCamelCase ):
  if args.no_teacher:
    lowercase     :Tuple   						=    TranslationModule if "translation" in args.task else SummarizationModule
  else:  # DISTILL WITH TEACHER
    lowercase     :List[Any]   						=    TranslationDistiller if "translation" in args.task else SummarizationDistiller
  lowercase     :str   						=    module_cls.__name__
  print(F"using module {args.setup_cls}" )
  lowercase     :List[str]   						=    module_cls(lowerCamelCase )
  return model



def    UpperCAmelCase__	(  lowerCamelCase ):
  Path(args.output_dir ).mkdir(exist_ok=lowerCamelCase )
  check_output_dir(lowerCamelCase,      expected_items=3 )

  lowercase     :Tuple   						=    create_module(lowerCamelCase )
  return ft_main(lowerCamelCase,      model=lowerCamelCase )


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

     distill_main(args)









import decimal



def    UpperCAmelCase__	(  ):
  lowercase     :int   						=    0
  lowercase     :str   						=    decimal.Context(prec=105 )
  for i in range(2,      100 ):
    lowercase     :int   						=    decimal.Decimal(lowerCamelCase )
    lowercase     :str   						=    number.sqrt(lowerCamelCase )
    if len(str(lowerCamelCase ) ) > 1:
      answer += int(str(lowerCamelCase )[0] )
      lowercase     :int   						=    str(lowerCamelCase )[2:101]
      answer += sum(int(lowerCamelCase ) for x in sqrt_number_str )
  return answer


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     print(f'''{solution() = }''')



