



'''simple docstring'''






import argparse
import random

import joblib
import numpy as np
import torch
from igf.igf import (
    SecondaryLearner,
    collect_objective_set,
    compute_perplexity,
    generate_datasets,
    load_gpta,
    recopy_gpta,
    set_seed,
    train_secondary_learner,
)
from torch.utils.data import DataLoader, RandomSampler

from transformers import GPTaLMHeadModel
def       lowerCamelCase__       (						_A=32 ,			_A=10 ,			_A=100 ,			_A=1026 ,			_A=True ,			_A="data/tokenized_stories_train_wikitext103.jbl" ,			_A="igf_context_pairs.jbl" ,			):
  set_seed(3				)
  # generate train_data and objective_set
  a   ,	a						:					Optional[int]        =     generate_datasets(
      _SCREAMING_SNAKE_CASE ,			_SCREAMING_SNAKE_CASE ,			number=_SCREAMING_SNAKE_CASE ,			min_len=1026 ,			trim=_SCREAMING_SNAKE_CASE				)
  # keeps model same across runs
  set_seed(4				)
  # model, lm_optimizer, lm_scheduler = recopy_gpt2(model, device, max_steps) # store original model weights
  # can we train on GPU?
  a						:					int        =     torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'				)

  # load pretrained model
  a						:					Optional[Any]        =     load_gpta('gpt2'				).to(_SCREAMING_SNAKE_CASE				)
  print('computing perplexity on objective set'				)
  a						:					Optional[Any]        =     compute_perplexity(_SCREAMING_SNAKE_CASE ,			_SCREAMING_SNAKE_CASE ,			_SCREAMING_SNAKE_CASE				).item()
  print('perplexity on objective set:' ,			_SCREAMING_SNAKE_CASE				)

  # collect igf pairs and save to file demo.jbl
  collect_objective_set(_SCREAMING_SNAKE_CASE ,			_SCREAMING_SNAKE_CASE ,			_SCREAMING_SNAKE_CASE ,			_SCREAMING_SNAKE_CASE ,			_SCREAMING_SNAKE_CASE ,			_SCREAMING_SNAKE_CASE ,			_SCREAMING_SNAKE_CASE ,			_SCREAMING_SNAKE_CASE				)

  # clean up, delete model and data we don't need anymore
  del model, train_data, objective_set
  torch.cuda.empty_cache()
def       lowerCamelCase__       (						_A ,			_A=15 ,			_A=128 ,			_A=100 ,			_A="igf_model.pt" ,			):
  set_seed(42				)

  # Load pre-trained model
  a						:					str        =     GPTaLMHeadModel.from_pretrained('gpt2'				)

  # Initialize secondary learner to use embedding weights of model
  a						:					Optional[int]        =     SecondaryLearner(_SCREAMING_SNAKE_CASE				)

  # Train secondary learner
  a						:					List[str]        =     train_secondary_learner(
      _SCREAMING_SNAKE_CASE ,			_SCREAMING_SNAKE_CASE ,			max_epochs=_SCREAMING_SNAKE_CASE ,			batch_size=_SCREAMING_SNAKE_CASE ,			eval_freq=100 ,			igf_model_path=_SCREAMING_SNAKE_CASE ,			)

  del model, secondary_learner_train_data
  torch.cuda.empty_cache()

  return secondary_learner
def       lowerCamelCase__       (						_A ,			_A ,			_A ,			_A=32 ,			_A=1000 ,			_A=16 ,			_A=1.0 ,			_A=recopy_gpta ,			_A=None ,			_A=10 ,			_A="gpt2_finetuned.pt" ,			):
  a						:					Any        =     torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'				)
  a						:					List[Any]        =     RandomSampler(_SCREAMING_SNAKE_CASE				)
  a						:					List[Any]        =     DataLoader(_SCREAMING_SNAKE_CASE ,			sampler=_SCREAMING_SNAKE_CASE				)

  a						:					Union[str, Any]        =     max_steps // (len(_SCREAMING_SNAKE_CASE				)) + 1
  a						:					Union[str, Any]        =     0
  a						:					List[str]        =     torch.zeros((1, context_len) ,			dtype=torch.long ,			device=_SCREAMING_SNAKE_CASE				)
  a   ,	a   ,	a						:					Dict        =     recopy_model(_SCREAMING_SNAKE_CASE ,			_SCREAMING_SNAKE_CASE ,			_SCREAMING_SNAKE_CASE				)

  model.train()
  if secondary_learner is not None:
    secondary_learner.to(_SCREAMING_SNAKE_CASE				)
    secondary_learner.eval()
  a						:					str        =     []
  a						:					Tuple        =     0

  a						:					List[str]        =     []
  a						:					Optional[int]        =     []

  # Compute the performance of the transformer model at the beginning
  a						:					Tuple        =     compute_perplexity(_SCREAMING_SNAKE_CASE ,			_SCREAMING_SNAKE_CASE ,			_SCREAMING_SNAKE_CASE				)
  test_perps.append(_SCREAMING_SNAKE_CASE				)
  print('Test perplexity, step' ,			_SCREAMING_SNAKE_CASE ,			':' ,			_SCREAMING_SNAKE_CASE				)
  for epoch in range(int(_SCREAMING_SNAKE_CASE				)				):
    for step, example in enumerate(_SCREAMING_SNAKE_CASE				):
      torch.cuda.empty_cache()
      a						:					Tuple        =     random.randint(0 ,			example.size(2				) - context_len - 1				)
      a						:					List[str]        =     example[0, 0, start : start + context_len]
      lm_optimizer.zero_grad()
      a						:					Dict        =     model(_SCREAMING_SNAKE_CASE ,			labels=_SCREAMING_SNAKE_CASE				)
      a						:					List[Any]        =     True

      if secondary_learner is not None:
        a						:					Optional[Any]        =     secondary_learner.forward(
            torch.tensor(_SCREAMING_SNAKE_CASE ,			dtype=torch.long ,			device=_SCREAMING_SNAKE_CASE				).unsqueeze(0				)				)[0].item()
        observed_qs.append(float(_SCREAMING_SNAKE_CASE				)				)

        # Here we implement the simple non-constant threshold for the predicted IG(X) value
        # We will decay the selectivity of our secondary learner filter from
        # 1 standard deviation above average to 1 below average after 10 batches.

        if global_step == 10:
          a						:					Union[str, Any]        =     -1
        if predicted_q < threshold:
          a						:					Tuple        =     False

            # If we passed the filter, add the context to the batch!
      if do_backprop:
        contexts.append(np.array(context.cpu()				)				)
        a						:					List[str]        =     outputs[0]
        lm_loss.backward()
        examples += 1

      del outputs

      # Once the batch is filled with enough contexts, backprop on the batch.
      if examples == batch_size:
        torch.cuda.empty_cache()
        a						:					List[str]        =     0
        # Do LM backprop
        torch.nn.utils.clip_grad_norm_(model.parameters() ,			3.0				)
        lm_optimizer.step()
        lm_scheduler.step()  # Update learning rate schedule
        global_step += 1
        # Compute the performance of the transformer model at this batch
        if global_step % eval_interval == 0:
          a						:					Tuple        =     compute_perplexity(_SCREAMING_SNAKE_CASE ,			_SCREAMING_SNAKE_CASE ,			_SCREAMING_SNAKE_CASE				)
          test_perps.append(_SCREAMING_SNAKE_CASE				)

          print('Test perplexity, step' ,			_SCREAMING_SNAKE_CASE ,			':' ,			_SCREAMING_SNAKE_CASE				)
            # Break out of the loop after 60 batches
      if max_steps > 0 and global_step > 60:
        break
    if max_steps > 0 and global_step > 60:
      break

    # save finetuned transformer model
  torch.save(model.state_dict() ,			_SCREAMING_SNAKE_CASE				)
  torch.cuda.empty_cache()
  # Do some cleaning up so we can reinitialize for the next run of this function
  del lm_optimizer
  del lm_scheduler
  return model
def       lowerCamelCase__       (						):
  a						:					Tuple        =     argparse.ArgumentParser(description='Fine-tune a transformer model with IGF on a language modeling task'				)

  # Required parameters
  parser.add_argument(
      '--data_dir' ,			default=_SCREAMING_SNAKE_CASE ,			type=_SCREAMING_SNAKE_CASE ,			required=_SCREAMING_SNAKE_CASE ,			help='The input data dir. Should contain data files for WikiText.' ,			)
  parser.add_argument(
      '--model_name_or_path' ,			default=_SCREAMING_SNAKE_CASE ,			type=_SCREAMING_SNAKE_CASE ,			required=_SCREAMING_SNAKE_CASE ,			help='Path to pretrained model or model identifier from huggingface.co/models' ,			)
  parser.add_argument(
      '--data_file' ,			type=_SCREAMING_SNAKE_CASE ,			default=_SCREAMING_SNAKE_CASE ,			help=(
          'A jbl file containing tokenized data which can be split as objective dataset, '
          'train_dataset and test_dataset.'
      ) ,			)

  parser.add_argument(
      '--igf_data_file' ,			type=_SCREAMING_SNAKE_CASE ,			default=_SCREAMING_SNAKE_CASE ,			help='A jbl file containing the context and information gain pairs to train secondary learner.' ,			)

  parser.add_argument(
      '--output_dir' ,			default=_SCREAMING_SNAKE_CASE ,			type=_SCREAMING_SNAKE_CASE ,			required=_SCREAMING_SNAKE_CASE ,			help='The output directory where the final fine-tuned model is stored.' ,			)

  parser.add_argument(
      '--tokenizer_name' ,			default=_SCREAMING_SNAKE_CASE ,			type=_SCREAMING_SNAKE_CASE ,			help='Pretrained tokenizer name or path if not the same as model_name' ,			)
  parser.add_argument('--seed' ,			type=_SCREAMING_SNAKE_CASE ,			default=_SCREAMING_SNAKE_CASE ,			help='A seed for reproducible training.'				)

  parser.add_argument(
      '--context_len' ,			default=32 ,			type=_SCREAMING_SNAKE_CASE ,			help=(
          'The maximum total input sequence length after tokenization. Sequences longer '
          'than this will be truncated, sequences shorter will be padded.'
      ) ,			)

  parser.add_argument(
      '--size_objective_set' ,			default=100 ,			type=_SCREAMING_SNAKE_CASE ,			help='number of articles that are long enough to be used as our objective set' ,			)
  parser.add_argument(
      '--eval_freq' ,			default=100 ,			type=_SCREAMING_SNAKE_CASE ,			help='secondary model evaluation is triggered at eval_freq'				)

  parser.add_argument('--max_steps' ,			default=1000 ,			type=_SCREAMING_SNAKE_CASE ,			help='To calculate training epochs'				)

  parser.add_argument(
      '--secondary_learner_batch_size' ,			default=128 ,			type=_SCREAMING_SNAKE_CASE ,			help='batch size of training data for secondary learner' ,			)

  parser.add_argument(
      '--batch_size' ,			default=16 ,			type=_SCREAMING_SNAKE_CASE ,			help='batch size of training data of language model(gpt2) '				)

  parser.add_argument(
      '--eval_interval' ,			default=10 ,			type=_SCREAMING_SNAKE_CASE ,			help=(
          'decay the selectivity of our secondary learner filter from'
          '1 standard deviation above average to 1 below average after 10 batches'
      ) ,			)

  parser.add_argument(
      '--number' ,			default=100 ,			type=_SCREAMING_SNAKE_CASE ,			help='The number of examples split to be used as objective_set/test_data'				)

  parser.add_argument(
      '--min_len' ,			default=1026 ,			type=_SCREAMING_SNAKE_CASE ,			help='The minimum length of the article to be used as objective set'				)

  parser.add_argument(
      '--secondary_learner_max_epochs' ,			default=15 ,			type=_SCREAMING_SNAKE_CASE ,			help='number of epochs to train secondary learner'				)

  parser.add_argument('--trim' ,			default=_SCREAMING_SNAKE_CASE ,			type=_SCREAMING_SNAKE_CASE ,			help='truncate the example if it exceeds context length'				)

  parser.add_argument(
      '--threshold' ,			default=1.0 ,			type=_SCREAMING_SNAKE_CASE ,			help=(
          'The threshold value used by secondary learner to filter the train_data and allow only'
          ' informative data as input to the model'
      ) ,			)

  parser.add_argument('--finetuned_model_name' ,			default='gpt2_finetuned.pt' ,			type=_SCREAMING_SNAKE_CASE ,			help='finetuned_model_name'				)

  parser.add_argument(
      '--recopy_model' ,			default=_SCREAMING_SNAKE_CASE ,			type=_SCREAMING_SNAKE_CASE ,			help='Reset the model to the original pretrained GPT-2 weights after each iteration' ,			)

  # function calls
  # Collecting *n* pairs of context and information gain(X, IG(X)) for training the secondary learner
  generate_n_pairs(
      context_len=32 ,			max_steps=10 ,			size_objective_set=100 ,			min_len=1026 ,			trim=_SCREAMING_SNAKE_CASE ,			data_file='data/tokenized_stories_train_wikitext103.jbl' ,			igf_data_file='igf_context_pairs.jbl' ,			)

  # Load train data for secondary learner
  a						:					int        =     joblib.load('data/IGF_values.jbl'				)

  # Train secondary learner
  a						:					int        =     training_secondary_learner(
      _SCREAMING_SNAKE_CASE ,			secondary_learner_max_epochs=15 ,			secondary_learner_batch_size=128 ,			eval_freq=100 ,			igf_model_path='igf_model.pt' ,			)

  # load pretrained gpt2 model
  a						:					Optional[int]        =     GPTaLMHeadModel.from_pretrained('gpt2'				)
  set_seed(42				)

  # Generate train and test data to train and evaluate gpt2 model
  a   ,	a						:					int        =     generate_datasets(
      context_len=32 ,			file='data/tokenized_stories_train_wikitext103.jbl' ,			number=100 ,			min_len=1026 ,			trim=_SCREAMING_SNAKE_CASE				)

  # fine-tuning of the gpt2 model using igf (Information Gain Filtration)
  finetune(
      _SCREAMING_SNAKE_CASE ,			_SCREAMING_SNAKE_CASE ,			_SCREAMING_SNAKE_CASE ,			context_len=32 ,			max_steps=1000 ,			batch_size=16 ,			threshold=1.0 ,			recopy_model=_SCREAMING_SNAKE_CASE ,			secondary_learner=_SCREAMING_SNAKE_CASE ,			eval_interval=10 ,			finetuned_model_name='gpt2_finetuned.pt' ,			)


if __name__ == "__main__":
     main()






import random

class 		UpperCamelCase__ :



     '''simple docstring'''

     @staticmethod
     def 				SCREAMING_SNAKE_CASE__  (							lowerCamelCase__						:    str       )     ->		tuple[list[int], list[int]]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						[ord(lowerCamelCase__       ) for i in text]
           SCREAMING_SNAKE_CASE		=						[]
           SCREAMING_SNAKE_CASE		=						[]
           for i in plain:
                 SCREAMING_SNAKE_CASE		=						random.randint(1						,300       )
                 SCREAMING_SNAKE_CASE		=						(i + k) * k
                 cipher.append(lowerCamelCase__       )
                 key.append(lowerCamelCase__       )
           return cipher, key






     @staticmethod
     def 				SCREAMING_SNAKE_CASE__  (							lowerCamelCase__						:    list[int]						,lowerCamelCase__						:    list[int]       )     ->		str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						[]
           for i in range(len(lowerCamelCase__       )       ):
                 SCREAMING_SNAKE_CASE		=						int((cipher[i] - (key[i]) ** 2) / key[i]       )
                 plain.append(chr(lowerCamelCase__       )       )
           return "".join(lowerCamelCase__       )


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE_   ,						SCREAMING_SNAKE_CASE_						   =       Onepad().encrypt("""Hello""")
      print(c, k)
      print(Onepad().decrypt(c, k))


