





import random
import sys

_snake_case				= """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""


def _A   (    ):
   lowercase__    =  input("Enter message: "       )
   lowercase__    =  "LFWOAYUISVKMNXPBDCRJTQEGHZ"
   lowercase__    =  input("Encrypt/Decrypt [e/d]: "       )

   check_valid_key(__magic_name__       )

   if resp.lower().startswith("e"       ):
      lowercase__    =  "encrypt"
      lowercase__    =  encrypt_message(__magic_name__  ,					__magic_name__       )
   elif resp.lower().startswith("d"       ):
      lowercase__    =  "decrypt"
      lowercase__    =  decrypt_message(__magic_name__  ,					__magic_name__       )

   print(f'''\n{mode.title()}ion: \n{translated}'''       )


def _A   (    __magic_name__       ):
   lowercase__    =  list(__magic_name__       )
   lowercase__    =  list(__magic_name__       )
   key_list.sort()
   letters_list.sort()

   if key_list != letters_list:
      sys.exit("Error in the key or symbol set."       )


def _A   (    __magic_name__  ,					__magic_name__       ):
   return translate_message(__magic_name__  ,					__magic_name__  ,					"encrypt"       )


def _A   (    __magic_name__  ,					__magic_name__       ):
   return translate_message(__magic_name__  ,					__magic_name__  ,					"decrypt"       )


def _A   (    __magic_name__  ,					__magic_name__  ,					__magic_name__       ):
   lowercase__    =  ""
   lowercase__    =  LETTERS
   lowercase__    =  key

   if mode == "decrypt":
      lowercase__   ,							lowercase__    =  chars_b, chars_a

   for symbol in message:
      if symbol.upper() in chars_a:
         lowercase__    =  chars_a.find(symbol.upper()       )
         if symbol.isupper():
            translated += chars_b[sym_index].upper()
         else:
            translated += chars_b[sym_index].lower()
      else:
         translated += symbol

   return translated


def _A   (    ):
   lowercase__    =  list(__magic_name__       )
   random.shuffle(__magic_name__       )
   return "".join(__magic_name__       )


if __name__ == "__main__":
  main()













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							(   lowercase_					):
  __lowerCamelCase		=			['loss', 'ce_loss', 'mlm_loss', 'hid_loss_enc', 'hid_loss_dec']







  def __init__(			self  :List[str]    ,							_lowercase  :Union[str, Any]					):






     '''simple docstring'''





     assert Path(hparams.data_dir					).exists()
     lowercase__    =  Path(hparams.output_dir					)
     self.output_dir.mkdir(exist_ok=_lowercase					)

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

     lowercase__    =  str(_lowercase					)  # Tell lightning we are training the student
     lowercase__    =  AutoModelForSeqaSeqLM.from_pretrained(hparams.teacher					).eval()
     use_task_specific_params(_lowercase    ,							hparams.task					)  # We copy good generation parameters to student by default
     if hparams.student is not None:
        lowercase__    =  AutoModelForSeqaSeqLM.from_pretrained(hparams.student					)
        use_task_specific_params(_lowercase    ,							hparams.task					)
        lowercase__   ,							lowercase__    =  None, None
     else:
        lowercase__   ,							lowercase__   ,							lowercase__    =  create_student_by_copying_alternating_layers(
            _lowercase    ,							e=hparams.student_encoder_layers    ,							d=hparams.student_decoder_layers    ,							save_path=_lowercase					)

     if hparams.length_penalty != -1:
        lowercase__    =  hparams.length_penalty
     lowercase__    =  hparams.teacher  # Use teacher's tokenizer
     super().__init__(_lowercase    ,							model=_lowercase    ,							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__    =  len(student.get_encoder().block					)
        lowercase__    =  len(student.get_decoder().block					)
        lowercase__    =  len(teacher.get_encoder().block					)
        lowercase__    =  len(teacher.get_decoder().block					)
     else:
        lowercase__    =  student.config.encoder_layers
        lowercase__    =  student.config.decoder_layers
        lowercase__    =  teacher.config.encoder_layers
        lowercase__    =  teacher.config.decoder_layers

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

     lowercase__   ,							lowercase__    =  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__    =  get_layers_to_supervise(
               n_student=len(self.e_layer_ids					)    ,							n_teacher=_lowercase					)
           lowercase__    =  get_layers_to_supervise(
               n_student=len(self.d_layer_ids					)    ,							n_teacher=_lowercase					)
        else:  # student layer should emulate hidden states of the teacher layer it was copied from
           lowercase__    =  self.e_layer_ids
           lowercase__    =  self.d_layer_ids
     else:
        lowercase__    =  None
        lowercase__    =  None

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







  def   UpperCAmelCase     (			self  :Union[str, Any]    ,							_lowercase  :Optional[int]    ,							_lowercase  :Optional[int]    ,							_lowercase  :Any					):






     '''simple docstring'''





     lowercase__    =  mask[:, :, None].expand_as(_lowercase					)
     lowercase__    =  s_logits.size(-1					)
     lowercase__    =  torch.masked_select(_lowercase    ,							_lowercase					)  # (bs * seq_length * voc_size) modulo the 1s in mask
     lowercase__    =  torch.masked_select(_lowercase    ,							_lowercase					)  # (bs * seq_length * voc_size) modulo the 1s in mask
     lowercase__    =  s_logits_slct.view(-1    ,							_lowercase					)  # (bs * seq_length, voc_size) modulo the 1s in mask
     lowercase__    =  t_logits_slct.view(-1    ,							_lowercase					)  # (bs * seq_length, voc_size) modulo the 1s in mask
     assert t_logits_slct.size() == s_logits_slct.size()
     lowercase__    =  (
         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   UpperCAmelCase     (			_lowercase  :Tuple    ,							_lowercase  :List[Any]					):






     '''simple docstring'''





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







  def   UpperCAmelCase     (			self  :Optional[Any]    ,							_lowercase  :dict					):






     '''simple docstring'''





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

     # noinspection PyCallingNonCallable
     lowercase__    =  self(
         _lowercase    ,							attention_mask=_lowercase    ,							decoder_input_ids=_lowercase    ,							output_hidden_states=self.do_calc_hidden_loss    ,							output_attentions=_lowercase    ,							use_cache=_lowercase    ,							)
     lowercase__    =  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__    =  nn.CrossEntropyLoss(ignore_index=_lowercase					)
        lowercase__    =  loss_fct(lm_logits.view(-1    ,							lm_logits.shape[-1]					)    ,							labels.view(-1					)					)
     else:
        lowercase__    =  nn.functional.log_softmax(_lowercase    ,							dim=-1					)
        lowercase__   ,							lowercase__    =  label_smoothed_nll_loss(
            _lowercase    ,							_lowercase    ,							self.hparams.label_smoothing    ,							ignore_index=_lowercase					)

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

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

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

     lowercase__    =  (
         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   UpperCAmelCase     (			_lowercase  :int    ,							_lowercase  :Union[str, Any]    ,							_lowercase  :List[Any]    ,							_lowercase  :Any    ,							_lowercase  :Optional[Any]					):






     '''simple docstring'''





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


def _A   (    __magic_name__       ):
   # 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=__magic_name__       )
   parser.add_argument("--alpha_ce"  ,					default=0.8  ,					type=__magic_name__       )
   parser.add_argument("--alpha_mlm"  ,					default=0.2  ,					type=__magic_name__       )
   parser.add_argument("--alpha_hid"  ,					default=0.0  ,					type=__magic_name__  ,					required=__magic_name__       )
   parser.add_argument("--student"  ,					type=__magic_name__  ,					required=__magic_name__       )
   parser.add_argument("--student_decoder_layers"  ,					default=12  ,					type=__magic_name__  ,					required=__magic_name__       )
   parser.add_argument("--student_encoder_layers"  ,					default=12  ,					type=__magic_name__  ,					required=__magic_name__       )
   parser.add_argument("--no_teacher"  ,					action="store_true"  ,					default=__magic_name__       )
   parser.add_argument("--length_penalty"  ,					type=__magic_name__  ,					default=-1       )
   parser.add_argument("--supervise_forward"  ,					action="store_true"  ,					default=__magic_name__       )
   parser.add_argument("--normalize_hidden"  ,					action="store_true"  ,					default=__magic_name__       )





class 					lowerCAmelCase							(   lowercase_					):
  __lowerCamelCase		=			'translation'
  __lowerCamelCase		=			['bleu']
  __lowerCamelCase		=			'bleu'







  def __init__(			self  :int    ,							_lowercase  :int    ,							**_lowercase  :Union[str, Any]					):






     '''simple docstring'''





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







  def   UpperCAmelCase     (			self  :str    ,							_lowercase  :List[str]    ,							_lowercase  :Optional[int]					):






     '''simple docstring'''





     return calculate_bleu(_lowercase    ,							_lowercase					)







  @staticmethod
  def   UpperCAmelCase     (			_lowercase  :str    ,							_lowercase  :List[Any]					):






     '''simple docstring'''





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


def _A   (    __magic_name__       ):
   if args.no_teacher:
      lowercase__    =  TranslationModule if "translation" in args.task else SummarizationModule
   else:  # DISTILL WITH TEACHER
      lowercase__    =  TranslationDistiller if "translation" in args.task else SummarizationDistiller
   lowercase__    =  module_cls.__name__
   print(f'''using module {args.setup_cls}'''       )
   lowercase__    =  module_cls(__magic_name__       )
   return model


def _A   (    __magic_name__       ):
   Path(args.output_dir       ).mkdir(exist_ok=__magic_name__       )
   check_output_dir(__magic_name__  ,					expected_items=3       )

   lowercase__    =  create_module(__magic_name__       )
   return ft_main(__magic_name__  ,					model=__magic_name__       )


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

  distill_main(args)






