
"""simple docstring"""







SCREAMING_SNAKE_CASE_  					= (1, 153, 370, 371, 1634, 24678051, 115132219018763992565095597973971522401)
SCREAMING_SNAKE_CASE_  					= (-153, -1, 0, 1.2, 200, "A", [], {}, None)
def       __UpperCAmelCase		(		a_      :		int					)  ->							bool:
   if not isinstance(a_     ,	a_					) or n < 1:
      return False

   # Initialization of sum and number of digits.
   _UpperCAmelCase 						=       0
   _UpperCAmelCase 						=       0
   _UpperCAmelCase 						=       n
   # Calculation of digits of the number
   while temp > 0:
      number_of_digits += 1
      temp //= 10
   # Dividing number into separate digits and find Armstrong number
   _UpperCAmelCase 						=       n
   while temp > 0:
      _UpperCAmelCase 						=       temp % 10
      total += rem**number_of_digits
      temp //= 10
   return n == total
def       __UpperCAmelCase		(		a_      :		int					)  ->							bool:
   if not isinstance(a_     ,	a_					) or n < 1:
      return False

   # Init a "histogram" of the digits
   _UpperCAmelCase 						=       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
   _UpperCAmelCase 						=       0
   _UpperCAmelCase 						=       0
   _UpperCAmelCase 						=       n
   while temp > 0:
      _UpperCAmelCase						,				_UpperCAmelCase 						=       divmod(a_     ,	10					)
      digit_histogram[rem] += 1
      digit_total += 1

   for cnt, i in zip(a_     ,	range(len(a_					)					)					):
      total += cnt * i**digit_total

   return n == total
def       __UpperCAmelCase		(		a_      :		int					)  ->							bool:
   if not isinstance(a_     ,	a_					) or n < 1:
      return False
   _UpperCAmelCase 						=       len(str(a_					)					)  # the power that all digits will be raised to
   # check if sum of each digit multiplied expo times is equal to number
   return n == sum(int(a_					) ** expo for i in str(a_					)					)
def       __UpperCAmelCase		(		)  ->							List[Any]:
   _UpperCAmelCase 						=       int(input("Enter an integer to see if it is an Armstrong number: "					).strip()					)
   print(F'''{num} is {'' if armstrong_number(a_					) else 'not '}an Armstrong number.'''					)
   print(F'''{num} is {'' if narcissistic_number(a_					) else 'not '}an Armstrong number.'''					)
   print(F'''{num} is {'' if pluperfect_number(a_					) else 'not '}an Armstrong number.'''					)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()



"""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      (       lowerCAmelCase_      ):
       _lowerCamelCase				:				str   			=      ['loss', 'ce_loss', 'mlm_loss', 'hid_loss_enc', 'hid_loss_dec']







       def __init__(       self				,lowerCamelCase_		):



          '''simple docstring'''

          assert Path(hparams.data_dir		).exists()
          _UpperCAmelCase 						=       Path(hparams.output_dir		)
          self.output_dir.mkdir(exist_ok=lowerCamelCase_		)

          _UpperCAmelCase 						=       self.output_dir.joinpath("student"		)

          _UpperCAmelCase 						=       str(lowerCamelCase_		)  # Tell lightning we are training the student
          _UpperCAmelCase 						=       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 						=       AutoModelForSeqaSeqLM.from_pretrained(hparams.student		)
             use_task_specific_params(lowerCamelCase_				,hparams.task		)
             _UpperCAmelCase						,				_UpperCAmelCase 						=       None, None
          else:
             _UpperCAmelCase						,				_UpperCAmelCase						,				_UpperCAmelCase 						=       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 						=       hparams.length_penalty
          _UpperCAmelCase 						=       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 						=       len(student.get_encoder().block		)
             _UpperCAmelCase 						=       len(student.get_decoder().block		)
             _UpperCAmelCase 						=       len(teacher.get_encoder().block		)
             _UpperCAmelCase 						=       len(teacher.get_decoder().block		)
          else:
             _UpperCAmelCase 						=       student.config.encoder_layers
             _UpperCAmelCase 						=       student.config.decoder_layers
             _UpperCAmelCase 						=       teacher.config.encoder_layers
             _UpperCAmelCase 						=       teacher.config.decoder_layers

          _UpperCAmelCase 						=       not (hparams.student is None or hparams.teacher == hparams.student)
          _UpperCAmelCase 						=       (not self.different_base_models) and hparams.alpha_hid > 0
          _UpperCAmelCase 						=       self.different_base_models or (student_encoder_layers != teacher_encoder_layers)
          # self.different_encoder determines whether we need to run the teacher encoder
          _UpperCAmelCase 						=       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 						=       list(range(lowerCamelCase_		)		)
          if d_layer_ids is None:
             _UpperCAmelCase 						=       list(range(lowerCamelCase_		)		)

          _UpperCAmelCase						,				_UpperCAmelCase 						=       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 						=       get_layers_to_supervise(
                    n_student=len(self.e_layer_ids		)				,n_teacher=lowerCamelCase_		)
                _UpperCAmelCase 						=       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 						=       self.e_layer_ids
                _UpperCAmelCase 						=       self.d_layer_ids
          else:
             _UpperCAmelCase 						=       None
             _UpperCAmelCase 						=       None

          _UpperCAmelCase 						=       nn.KLDivLoss(reduction="batchmean"		)
          _UpperCAmelCase 						=       2.0
          _UpperCAmelCase 						=       hparams.alpha_mlm
          _UpperCAmelCase 						=       hparams.alpha_ce
          _UpperCAmelCase 						=       hparams.alpha_hid
          gc.collect()
          torch.cuda.empty_cache()







       def  _snake_case      (       self				,lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_		):



          '''simple docstring'''

          _UpperCAmelCase 						=       mask[:, :, None].expand_as(lowerCamelCase_		)
          _UpperCAmelCase 						=       s_logits.size(-1		)
          _UpperCAmelCase 						=       torch.masked_select(lowerCamelCase_				,lowerCamelCase_		)  # (bs * seq_length * voc_size) modulo the 1s in mask
          _UpperCAmelCase 						=       torch.masked_select(lowerCamelCase_				,lowerCamelCase_		)  # (bs * seq_length * voc_size) modulo the 1s in mask
          _UpperCAmelCase 						=       s_logits_slct.view(-1				,lowerCamelCase_		)  # (bs * seq_length, voc_size) modulo the 1s in mask
          _UpperCAmelCase 						=       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 						=       (
              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      (       lowerCamelCase_				,lowerCamelCase_		):



          '''simple docstring'''

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







       def  _snake_case      (       self				,lowerCamelCase_		):



          '''simple docstring'''

          _UpperCAmelCase 						=       self.tokenizer.pad_token_id
          _UpperCAmelCase						,				_UpperCAmelCase						,				_UpperCAmelCase 						=       batch["input_ids"], batch["attention_mask"], batch["labels"]
          if isinstance(self.model				,lowerCamelCase_		):
             _UpperCAmelCase 						=       self.model._shift_right(lowerCamelCase_		)
          else:
             _UpperCAmelCase 						=       shift_tokens_right(lowerCamelCase_				,lowerCamelCase_		)

          # noinspection PyCallingNonCallable
          _UpperCAmelCase 						=       self(
              lowerCamelCase_				,attention_mask=lowerCamelCase_				,decoder_input_ids=lowerCamelCase_				,output_hidden_states=self.do_calc_hidden_loss				,output_attentions=lowerCamelCase_				,use_cache=lowerCamelCase_				,)
          _UpperCAmelCase 						=       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 						=       nn.CrossEntropyLoss(ignore_index=lowerCamelCase_		)
             _UpperCAmelCase 						=       loss_fct(lm_logits.view(-1				,lm_logits.shape[-1]		)				,labels.view(-1		)		)
          else:
             _UpperCAmelCase 						=       nn.functional.log_softmax(lowerCamelCase_				,dim=-1		)
             _UpperCAmelCase						,				_UpperCAmelCase 						=       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 						=       student_outputs[
              "encoder_last_hidden_state"
          ]  # use this unless self.different_base_models
          _UpperCAmelCase						,				_UpperCAmelCase 						=       zero_tensor(), zero_tensor()
          if self.different_encoder:  # compute encoder hidden state loss
             _UpperCAmelCase 						=       self.teacher.get_encoder()(
                 lowerCamelCase_				,attention_mask=lowerCamelCase_				,output_hidden_states=self.do_calc_hidden_loss				,)
             if self.different_base_models:
                _UpperCAmelCase 						=       all_teacher_encoder_outputs["last_hidden_state"]
             elif self.do_calc_hidden_loss:
                _UpperCAmelCase 						=       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 						=       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 						=       decoder_input_ids.ne(lowerCamelCase_		)
          _UpperCAmelCase 						=       self.calc_ce_loss(lowerCamelCase_				,lowerCamelCase_				,teacher_outputs["logits"]		)
          if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
             _UpperCAmelCase 						=       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 						=       (
              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      (       lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_		):



          '''simple docstring'''

          _UpperCAmelCase 						=       "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 						=       attention_mask.to(hidden_states[0]		)
          _UpperCAmelCase 						=       mask.sum() * hidden_states[0].size(-1		)
          _UpperCAmelCase 						=       torch.stack([hidden_states[i] for i in range(len(lowerCamelCase_		)		)]		)
          _UpperCAmelCase 						=       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 						=       nn.functional.layer_norm(lowerCamelCase_				,student_states.shape[1:]		)
             _UpperCAmelCase 						=       nn.functional.layer_norm(lowerCamelCase_				,teacher_states.shape[1:]		)
          _UpperCAmelCase 						=       nn.functional.mse_loss(lowerCamelCase_				,lowerCamelCase_				,reduction="none"		)
          _UpperCAmelCase 						=       (mse * mask.unsqueeze(0		).unsqueeze(-1		)).sum() / valid_count
          return masked_mse



def       __UpperCAmelCase		(		a_      :		List[str]					)  ->							Tuple:
   # 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      (       lowerCAmelCase_      ):
       _lowerCamelCase				:				Union[str, Any]   			=      'translation'
       _lowerCamelCase				:				Dict   			=      ['bleu']
       _lowerCamelCase				:				Any   			=      'bleu'







       def __init__(       self				,lowerCamelCase_				,**lowerCamelCase_		):



          '''simple docstring'''

          super().__init__(lowerCamelCase_				,**lowerCamelCase_		)
          assert hparams.src_lang is not None
          assert hparams.tgt_lang is not None
          _UpperCAmelCase 						=       hparams.src_lang
          _UpperCAmelCase 						=       hparams.tgt_lang
          if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer				,lowerCamelCase_		):
             _UpperCAmelCase 						=       self.tokenizer.lang_code_to_id[hparams.tgt_lang]







       def  _snake_case      (       self				,lowerCamelCase_				,lowerCamelCase_		):



          '''simple docstring'''

          return calculate_bleu(lowerCamelCase_				,lowerCamelCase_		)







       @staticmethod
       def  _snake_case      (       lowerCamelCase_				,lowerCamelCase_		):



          '''simple docstring'''

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



def       __UpperCAmelCase		(		a_      :		str					)  ->							Any:
   if args.no_teacher:
      _UpperCAmelCase 						=       TranslationModule if "translation" in args.task else SummarizationModule
   else:  # DISTILL WITH TEACHER
      _UpperCAmelCase 						=       TranslationDistiller if "translation" in args.task else SummarizationDistiller
   _UpperCAmelCase 						=       module_cls.__name__
   print(F'''using module {args.setup_cls}'''					)
   _UpperCAmelCase 						=       module_cls(a_					)
   return model
def       __UpperCAmelCase		(		a_      :		List[str]					)  ->							str:
   Path(args.output_dir					).mkdir(exist_ok=a_					)
   check_output_dir(a_     ,	expected_items=3					)

   _UpperCAmelCase 						=       create_module(a_					)
   return ft_main(a_     ,	model=a_					)


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE_  					= argparse.ArgumentParser()
  SCREAMING_SNAKE_CASE_  					= pl.Trainer.add_argparse_args(parser)
  SCREAMING_SNAKE_CASE_  					= SummarizationDistiller.add_model_specific_args(parser, os.getcwd())
  SCREAMING_SNAKE_CASE_  					= parser.parse_args()

  distill_main(args)

