"""simple docstring"""
import copy
import logging
import random

import joblib
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AdamW, GPTaLMHeadModel, get_linear_schedule_with_warmup


lowercase__   			=      logging.getLogger(__name__)







def 		__a  (      _SCREAMING_SNAKE_CASE  )      ->Any:
  random.seed(_SCREAMING_SNAKE_CASE  )
  np.random.seed(_SCREAMING_SNAKE_CASE  )
  torch.manual_seed(_SCREAMING_SNAKE_CASE  )
  torch.cuda.manual_seed_all(_SCREAMING_SNAKE_CASE  )







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->Union[str, Any]:
  model.eval()
  a__:	Any     				=      next(model.parameters()  ).device
  a__:	List[str]     				=      1
  a__:	List[Any]     				=      torch.zeros((eval_batch_size, context_len)						,	dtype=torch.long						,	device=_SCREAMING_SNAKE_CASE  )
  a__:	Optional[Any]     				=      DataLoader(_SCREAMING_SNAKE_CASE						,	shuffle=_SCREAMING_SNAKE_CASE						,	batch_size=_SCREAMING_SNAKE_CASE  )
  a__:	int     				=      torch.zeros(1						,	device=_SCREAMING_SNAKE_CASE  )
  a__:	int     				=      0
  for batch in eval_dataloader:
    batch.to(_SCREAMING_SNAKE_CASE  )
    # pad
    context.zero_()
    for i in range(_SCREAMING_SNAKE_CASE  ):
      a__:	Union[str, Any]     				=      batch[i]
    a__:	Optional[int]     				=      model(_SCREAMING_SNAKE_CASE						,	labels=_SCREAMING_SNAKE_CASE  )
    eval_loss += outputs[0].sum().item()
    nb_eval_examples += batch.size(0  )
  a__:	Any     				=      eval_loss / nb_eval_examples
  a__:	Dict     				=      torch.exp(_SCREAMING_SNAKE_CASE  )
  model.train()
  return perplexity







def 		__a  (      _SCREAMING_SNAKE_CASE="gpt2"  )      ->List[str]:
  a__:	List[Any]     				=      GPTaLMHeadModel.from_pretrained(_SCREAMING_SNAKE_CASE						,	output_hidden_states=_SCREAMING_SNAKE_CASE  )
  torch.save(model.state_dict()						,	model_name + 'local.pt'  )
  return model







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->Any:
  a__:	Optional[int]     				=      copy.deepcopy(_SCREAMING_SNAKE_CASE  )
  model.to(_SCREAMING_SNAKE_CASE  )

  a__:	Optional[int]     				=      ['bias', 'LayerNorm.weight']
  a__:	int     				=      [
      {
          'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay  )],
          'weight_decay': 0.0,
      },
      {'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay  )], 'weight_decay': 0.0},
  ]
  a__:	Any     				=      AdamW(_SCREAMING_SNAKE_CASE						,	lr=5e-5						,	eps=1e-8  )
  a__:	List[Any]     				=      get_linear_schedule_with_warmup(_SCREAMING_SNAKE_CASE						,	0						,	_SCREAMING_SNAKE_CASE  )
  torch.cuda.empty_cache()
  return model, lm_optimizer, lm_scheduler







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->Tuple:
  a__:	Dict     				=      np.array(_SCREAMING_SNAKE_CASE  ).mean()
  a__:	Optional[int]     				=      np.array(_SCREAMING_SNAKE_CASE  ).std()
  a__:	Optional[int]     				=      (real_perps - avg) / std
  a__:	int     				=      list(zip(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )  )
  joblib.dump(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE="dev.jbl"						,	_SCREAMING_SNAKE_CASE=recopy_gpta						,	)      ->Tuple:
  a__:	int     				=      []
  a__:	str     				=      []
  a__:	List[Any]     				=      []

  # Initialize the transformer model
  a__:	Optional[int]     				=      copy.deepcopy(_SCREAMING_SNAKE_CASE  )
  orig_model.to(device='cpu'  )
  torch.cuda.empty_cache()

  # Compute perplexity of initial transformer model for comparison
  model.train()
  a__				,       a__				,       a__:	Union[str, Any]     				=      recopy_model(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )

  for step in tqdm(range(_SCREAMING_SNAKE_CASE  )  ):
    a__:	Any     				=      torch.zeros((1, context_len)						,	dtype=torch.long						,	device=_SCREAMING_SNAKE_CASE  )
    a__:	Optional[int]     				=      random.choice(_SCREAMING_SNAKE_CASE  )
    a__:	Tuple     				=      random.randint(0						,	len(story[0]  ) - context_len - 1  )
    a__:	Optional[int]     				=      story[0][start : start + context_len]
    lm_optimizer.zero_grad()
    a__:	Dict     				=      model(_SCREAMING_SNAKE_CASE						,	labels=_SCREAMING_SNAKE_CASE  )
    a__:	List[Any]     				=      outputs[0]
    a__:	int     				=      compute_perplexity(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )
    model.train()
    lm_loss.backward()
    # Do LM backprop
    torch.nn.utils.clip_grad_norm_(model.parameters()						,	3.0  )
    lm_optimizer.step()
    lm_scheduler.step()  # Update learning rate schedule

    # Compute perplexity after back-propagating on the selected context
    a__:	str     				=      compute_perplexity(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )

    # Periodically save the stored (X, IG(X)) pairs
    if step % 1000 == 0 and step > 1:
      intermittent_save(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )

    # Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
    a__				,       a__				,       a__:	Tuple     				=      recopy_model(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )

    past_perps.append(past_perp.item()  )
    real_perps.append(orig_perp - real_perp.item()  )
    contexts.append(np.array(context.cpu()  )  )

  intermittent_save(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE="data/tokenized_stories_train_wikitext103.jbl"						,	_SCREAMING_SNAKE_CASE=100						,	_SCREAMING_SNAKE_CASE=1026						,	_SCREAMING_SNAKE_CASE=True  )      ->Optional[Any]:
  a__:	Optional[int]     				=      joblib.load(_SCREAMING_SNAKE_CASE  )
  print('data loaded'  )
  a__:	List[Any]     				=      []
  if trim:
    for i, example in enumerate(_SCREAMING_SNAKE_CASE  ):
      if len(example[0]  ) > min_len:
        a__:	Optional[Any]     				=      random.randint(0						,	len(example[0]  ) - context_len - 1  )
        objective_set.append(example[0, start : start + context_len]  )
      if len(_SCREAMING_SNAKE_CASE  ) >= number:
        break
    a__:	int     				=      []
    for j in range(i + 1						,	len(_SCREAMING_SNAKE_CASE  )  ):
      if len(data[j][0]  ) > min_len:
        train_data.append(data[j]  )
  else:
    a__:	Tuple     				=      data[0:number]
    a__:	int     				=      data[number:]

  joblib.dump(_SCREAMING_SNAKE_CASE						,	'objective_set.jbl'  )
  print('objective set saved'  )
  return train_data, objective_set







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE=50						,	_SCREAMING_SNAKE_CASE="secondary_learner.pt"  )      ->Any:
  a__:	str     				=      torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'  )
  # We will use the first 512 pairs from our dataset as a test set for
  # our secondary learner and the rest to train
  a__:	Dict     				=      train_dataset[:512]
  a__:	Union[str, Any]     				=      train_dataset[512:]
  a__:	Optional[int]     				=      DataLoader(_SCREAMING_SNAKE_CASE						,	shuffle=_SCREAMING_SNAKE_CASE						,	batch_size=_SCREAMING_SNAKE_CASE  )
  a__:	str     				=      DataLoader(_SCREAMING_SNAKE_CASE						,	shuffle=_SCREAMING_SNAKE_CASE						,	batch_size=_SCREAMING_SNAKE_CASE  )

  # secondary learner model set up
  a__:	Tuple     				=      nn.MSELoss()
  a__:	Any     				=      nn.MSELoss(reduction='sum'  )
  secondary_learner.to(_SCREAMING_SNAKE_CASE  )
  a__:	List[Any]     				=      torch.optim.Adam(secondary_learner.parameters()						,	lr=0.00_001  )
  secondary_learner.train()

  # TODO in original code this is written as number of actual batches seen
  # not number of items seen but other places it is number of items instead.
  # improve consistency! changed this to epochs for clarity
  a__:	Dict     				=      float('inf'  )
  # Iterate through batches until we've used max_steps batches
  for epoch in range(int(_SCREAMING_SNAKE_CASE  )  ):
    a__:	int     				=      0.0
    secondary_learner.train()
    for step, batch in enumerate(_SCREAMING_SNAKE_CASE  ):
      a__:	Any     				=      batch[0].to(_SCREAMING_SNAKE_CASE  )
      a__:	Any     				=      batch[1].to(_SCREAMING_SNAKE_CASE  )
      a__:	int     				=      secondary_learner(_SCREAMING_SNAKE_CASE  )
      q_optimizer.zero_grad()
      a__:	Tuple     				=      loss(_SCREAMING_SNAKE_CASE						,	real_q.float()  )
      q_loss.backward()
      q_optimizer.step()
      tr_q_loss += q_loss.item()

      # model trains fairly quickly so we won't wait for a full epoch
      # eval is triggered at eval_freq and end of epochs
      if (step % eval_freq == 0 and step > 0) or ((step + 1) == len(_SCREAMING_SNAKE_CASE  )):
        a__:	Union[str, Any]     				=      tr_q_loss / (step + 1)

        secondary_learner.eval()
        a__:	Optional[int]     				=      0.0
        a__:	Tuple     				=      0.0
        a__:	List[Any]     				=      []
        a__:	List[str]     				=      []
        # Compute performance of the secondary learner after this batch
        for stepa, batcha in enumerate(_SCREAMING_SNAKE_CASE  ):
          a__:	Dict     				=      batcha[0].to(_SCREAMING_SNAKE_CASE  )
          a__:	int     				=      batcha[1].to(_SCREAMING_SNAKE_CASE  )
          a__:	str     				=      secondary_learner(_SCREAMING_SNAKE_CASE  )
          q_lossa += test_loss(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  ).item()
          sum_qa += torch.sum(_SCREAMING_SNAKE_CASE  ).item()
          for ei, i in enumerate(predicted_qa.cpu().detach().numpy()  ):
            predicted.append(i.item()  )
          for ei, i in enumerate(real_qa.cpu().detach().numpy()  ):
            actual.append(i.item()  )

        q_lossa /= len(_SCREAMING_SNAKE_CASE  )
        print(
            'Epoch: '						,	_SCREAMING_SNAKE_CASE						,	'step: '						,	_SCREAMING_SNAKE_CASE						,	'Avg. q:'						,	sum_qa / len(_SCREAMING_SNAKE_CASE  )						,	'Train Loss: '						,	_SCREAMING_SNAKE_CASE						,	'Test Loss: '						,	_SCREAMING_SNAKE_CASE						,	)
        if q_lossa < best_test_loss:
          joblib.dump((predicted, actual)						,	'pred_vs_actual.jbl'  )
          torch.save(secondary_learner.state_dict()						,	_SCREAMING_SNAKE_CASE  )
          a__:	Optional[int]     				=      q_lossa

      secondary_learner.train()
  return secondary_learner




class   __snake_case			(	nn.Module							):



      def __init__(		self       ,     lowercase)      ->     int:





        '''simple docstring'''





        super(lowercase       ,     self).__init__()
        a__:	int     				=      model.transformer.wte
        a__:	int     				=      copy.deepcopy(model.transformer.wte.weight)
        a__:	int     				=      nn.Convad(self.embeddings.weight.size(1)       ,     2_56       ,     3       ,     padding=1)
        a__:	Optional[int]     				=      nn.Sequential(nn.Linear(2_56       ,     32)       ,     nn.Dropout(p=0.1)       ,     nn.Linear(32       ,     32)       ,     nn.Linear(32       ,     1))



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     Dict:





        '''simple docstring'''





        a__:	Union[str, Any]     				=      torch.max(self.conv(self.embeddings(lowercase).squeeze(1).transpose(1       ,     2))       ,     2)[0]
        a__:	List[str]     				=      self.fc(lowercase)
        return qs.squeeze(1)



      @classmethod
      def     lowerCamelCase_  (		cls       ,     lowercase       ,     lowercase)      ->     List[Any]:





        '''simple docstring'''





        a__:	Optional[Any]     				=      cls(lowercase)  # this calls __init__
        a__:	int     				=      torch.load(lowercase)
        secondary_learner.load_state_dict(lowercase)
        a__:	Optional[int]     				=      model.transformer.wte
        a__:	Any     				=      copy.deepcopy(model.transformer.wte.weight)
        return secondary_learner



"""simple docstring"""
import random
import sys

from . import cryptomath_module as cryptomath

lowercase__   			=      (
    r' !"#$%&\'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`'
    r'abcdefghijklmnopqrstuvwxyz{|}~'
)







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->None:
  if mode == "encrypt":
    if key_a == 1:
      sys.exit(
          'The affine cipher becomes weak when key '
          'A is set to 1. Choose different key'  )
    if key_b == 0:
      sys.exit(
          'The affine cipher becomes weak when key '
          'B is set to 0. Choose different key'  )
  if key_a < 0 or key_b < 0 or key_b > len(_SCREAMING_SNAKE_CASE  ) - 1:
    sys.exit(
        'Key A must be greater than 0 and key B must '
        F'be between 0 and {len(_SCREAMING_SNAKE_CASE  ) - 1}.'  )
  if cryptomath.gcd(_SCREAMING_SNAKE_CASE						,	len(_SCREAMING_SNAKE_CASE  )  ) != 1:
    sys.exit(
        F'Key A {key_a} and the symbol set size {len(_SCREAMING_SNAKE_CASE  )} '
        'are not relatively prime. Choose a different key.'  )







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->str:
  a__				,       a__:	Optional[Any]     				=      divmod(_SCREAMING_SNAKE_CASE						,	len(_SCREAMING_SNAKE_CASE  )  )
  check_keys(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	'encrypt'  )
  a__:	Dict     				=      ''
  for symbol in message:
    if symbol in SYMBOLS:
      a__:	int     				=      SYMBOLS.find(_SCREAMING_SNAKE_CASE  )
      cipher_text += SYMBOLS[(sym_index * key_a + key_b) % len(_SCREAMING_SNAKE_CASE  )]
    else:
      cipher_text += symbol
  return cipher_text







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->str:
  a__				,       a__:	List[str]     				=      divmod(_SCREAMING_SNAKE_CASE						,	len(_SCREAMING_SNAKE_CASE  )  )
  check_keys(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	'decrypt'  )
  a__:	List[Any]     				=      ''
  a__:	str     				=      cryptomath.find_mod_inverse(_SCREAMING_SNAKE_CASE						,	len(_SCREAMING_SNAKE_CASE  )  )
  for symbol in message:
    if symbol in SYMBOLS:
      a__:	List[str]     				=      SYMBOLS.find(_SCREAMING_SNAKE_CASE  )
      plain_text += SYMBOLS[
          (sym_index - key_b) * mod_inverse_of_key_a % len(_SCREAMING_SNAKE_CASE  )
      ]
    else:
      plain_text += symbol
  return plain_text







def 		__a  (      )      ->int:
  while True:
    a__:	int     				=      random.randint(2						,	len(_SCREAMING_SNAKE_CASE  )  )
    a__:	List[Any]     				=      random.randint(2						,	len(_SCREAMING_SNAKE_CASE  )  )
    if cryptomath.gcd(_SCREAMING_SNAKE_CASE						,	len(_SCREAMING_SNAKE_CASE  )  ) == 1 and key_b % len(_SCREAMING_SNAKE_CASE  ) != 0:
      return key_b * len(_SCREAMING_SNAKE_CASE  ) + key_b







def 		__a  (      )      ->None:
  a__:	Union[str, Any]     				=      input('Enter message: '  ).strip()
  a__:	List[Any]     				=      int(input('Enter key [2000 - 9000]: '  ).strip()  )
  a__:	List[Any]     				=      input('Encrypt/Decrypt [E/D]: '  ).strip().lower()

  if mode.startswith('e'  ):
    a__:	int     				=      'encrypt'
    a__:	List[str]     				=      encrypt_message(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )
  elif mode.startswith('d'  ):
    a__:	Dict     				=      'decrypt'
    a__:	Any     				=      decrypt_message(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )
  print(F'\n{mode.title()}ed text: \n{translated}'  )


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 # main()


