






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 		lowercase__  (				_UpperCamelCase=32       ,							_UpperCamelCase=10       ,							_UpperCamelCase=1_00       ,							_UpperCamelCase=10_26       ,							_UpperCamelCase=True       ,							_UpperCamelCase="data/tokenized_stories_train_wikitext103.jbl"       ,							_UpperCamelCase="igf_context_pairs.jbl"       ,							) ->			Optional[int]:
      """simple docstring"""

      set_seed(3)
      # generate train_data and objective_set
      UpperCamelCase       ,  UpperCamelCase					       =  generate_datasets(
          _UpperCamelCase       ,							_UpperCamelCase       ,							number=_UpperCamelCase       ,							min_len=10_26       ,							trim=_UpperCamelCase)
      # 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?
      UpperCamelCase					       =  torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')

      # load pretrained model
      UpperCamelCase					       =  load_gpta('gpt2').to(_UpperCamelCase)
      print('computing perplexity on objective set')
      UpperCamelCase					       =  compute_perplexity(_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase).item()
      print('perplexity on objective set:'       ,							_UpperCamelCase)

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

      # clean up, delete model and data we don't need anymore
      del model, train_data, objective_set
      torch.cuda.empty_cache()
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase=15       ,							_UpperCamelCase=1_28       ,							_UpperCamelCase=1_00       ,							_UpperCamelCase="igf_model.pt"       ,							) ->			Union[str, Any]:
      """simple docstring"""

      set_seed(42)

      # Load pre-trained model
      UpperCamelCase					       =  GPTaLMHeadModel.from_pretrained('gpt2')

      # Initialize secondary learner to use embedding weights of model
      UpperCamelCase					       =  SecondaryLearner(_UpperCamelCase)

      # Train secondary learner
      UpperCamelCase					       =  train_secondary_learner(
          _UpperCamelCase       ,							_UpperCamelCase       ,							max_epochs=_UpperCamelCase       ,							batch_size=_UpperCamelCase       ,							eval_freq=1_00       ,							igf_model_path=_UpperCamelCase       ,							)

      del model, secondary_learner_train_data
      torch.cuda.empty_cache()

      return secondary_learner
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase=32       ,							_UpperCamelCase=10_00       ,							_UpperCamelCase=16       ,							_UpperCamelCase=1.0       ,							_UpperCamelCase=recopy_gpta       ,							_UpperCamelCase=None       ,							_UpperCamelCase=10       ,							_UpperCamelCase="gpt2_finetuned.pt"       ,							) ->			str:
      """simple docstring"""

      UpperCamelCase					       =  torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
      UpperCamelCase					       =  RandomSampler(_UpperCamelCase)
      UpperCamelCase					       =  DataLoader(_UpperCamelCase       ,							sampler=_UpperCamelCase)

      UpperCamelCase					       =  max_steps // (len(_UpperCamelCase)) + 1
      UpperCamelCase					       =  0
      UpperCamelCase					       =  torch.zeros((1, context_len)       ,							dtype=torch.long       ,							device=_UpperCamelCase)
      UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase					       =  recopy_model(_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase)

      model.train()
      if secondary_learner is not None:
            secondary_learner.to(_UpperCamelCase)
            secondary_learner.eval()
      UpperCamelCase					       =  []
      UpperCamelCase					       =  0

      UpperCamelCase					       =  []
      UpperCamelCase					       =  []

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

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

                        # 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:
                              UpperCamelCase					       =  -1
                        if predicted_q < threshold:
                              UpperCamelCase					       =  False

            # If we passed the filter, add the context to the batch!
                  if do_backprop:
                        contexts.append(np.array(context.cpu()))
                        UpperCamelCase					       =  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()
                        UpperCamelCase					       =  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:
                              UpperCamelCase					       =  compute_perplexity(_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase)
                              test_perps.append(_UpperCamelCase)

                              print('Test perplexity, step'       ,							_UpperCamelCase       ,							':'       ,							_UpperCamelCase)
            # 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()       ,							_UpperCamelCase)
      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 		lowercase__  (				) ->			Optional[int]:
      """simple docstring"""

      UpperCamelCase					       =  argparse.ArgumentParser(description='Fine-tune a transformer model with IGF on a language modeling task')

      # Required parameters
      parser.add_argument(
          '--data_dir'       ,							default=_UpperCamelCase       ,							type=_UpperCamelCase       ,							required=_UpperCamelCase       ,							help='The input data dir. Should contain data files for WikiText.'       ,							)
      parser.add_argument(
          '--model_name_or_path'       ,							default=_UpperCamelCase       ,							type=_UpperCamelCase       ,							required=_UpperCamelCase       ,							help='Path to pretrained model or model identifier from huggingface.co/models'       ,							)
      parser.add_argument(
          '--data_file'       ,							type=_UpperCamelCase       ,							default=_UpperCamelCase       ,							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=_UpperCamelCase       ,							default=_UpperCamelCase       ,							help='A jbl file containing the context and information gain pairs to train secondary learner.'       ,							)

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

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

      parser.add_argument(
          '--context_len'       ,							default=32       ,							type=_UpperCamelCase       ,							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=1_00       ,							type=_UpperCamelCase       ,							help='number of articles that are long enough to be used as our objective set'       ,							)
      parser.add_argument(
          '--eval_freq'       ,							default=1_00       ,							type=_UpperCamelCase       ,							help='secondary model evaluation is triggered at eval_freq')

      parser.add_argument('--max_steps'       ,							default=10_00       ,							type=_UpperCamelCase       ,							help='To calculate training epochs')

      parser.add_argument(
          '--secondary_learner_batch_size'       ,							default=1_28       ,							type=_UpperCamelCase       ,							help='batch size of training data for secondary learner'       ,							)

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

      parser.add_argument(
          '--eval_interval'       ,							default=10       ,							type=_UpperCamelCase       ,							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=1_00       ,							type=_UpperCamelCase       ,							help='The number of examples split to be used as objective_set/test_data')

      parser.add_argument(
          '--min_len'       ,							default=10_26       ,							type=_UpperCamelCase       ,							help='The minimum length of the article to be used as objective set')

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

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

      parser.add_argument(
          '--threshold'       ,							default=1.0       ,							type=_UpperCamelCase       ,							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=_UpperCamelCase       ,							help='finetuned_model_name')

      parser.add_argument(
          '--recopy_model'       ,							default=_UpperCamelCase       ,							type=_UpperCamelCase       ,							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=1_00       ,							min_len=10_26       ,							trim=_UpperCamelCase       ,							data_file='data/tokenized_stories_train_wikitext103.jbl'       ,							igf_data_file='igf_context_pairs.jbl'       ,							)

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

      # Train secondary learner
      UpperCamelCase					       =  training_secondary_learner(
          _UpperCamelCase       ,							secondary_learner_max_epochs=15       ,							secondary_learner_batch_size=1_28       ,							eval_freq=1_00       ,							igf_model_path='igf_model.pt'       ,							)

      # load pretrained gpt2 model
      UpperCamelCase					       =  GPTaLMHeadModel.from_pretrained('gpt2')
      set_seed(42)

      # Generate train and test data to train and evaluate gpt2 model
      UpperCamelCase       ,  UpperCamelCase					       =  generate_datasets(
          context_len=32       ,							file='data/tokenized_stories_train_wikitext103.jbl'       ,							number=1_00       ,							min_len=10_26       ,							trim=_UpperCamelCase)

      # fine-tuning of the gpt2 model using igf (Information Gain Filtration)
      finetune(
          _UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							context_len=32       ,							max_steps=10_00       ,							batch_size=16       ,							threshold=1.0       ,							recopy_model=_UpperCamelCase       ,							secondary_learner=_UpperCamelCase       ,							eval_interval=10       ,							finetuned_model_name='gpt2_finetuned.pt'       ,							)


if __name__ == "__main__":
       main()











__magic_name__   :  Optional[Any]        =							0  # The first color of the flag.
__magic_name__   :  List[str]        =							1  # The second color of the flag.
__magic_name__   :  Dict        =							2  # The third color of the flag.
__magic_name__   :  Union[str, Any]        =							(red, white, blue)
def 		lowercase__  (				_UpperCamelCase) ->			list:
      """simple docstring"""

      if not sequence:
            return []
      if len(_UpperCamelCase) == 1:
            return list(_UpperCamelCase)
      UpperCamelCase					       =  0
      UpperCamelCase					       =  len(_UpperCamelCase) - 1
      UpperCamelCase					       =  0
      while mid <= high:
            if sequence[mid] == colors[0]:
                  UpperCamelCase       ,  UpperCamelCase					       =  sequence[mid], sequence[low]
                  low += 1
                  mid += 1
            elif sequence[mid] == colors[1]:
                  mid += 1
            elif sequence[mid] == colors[2]:
                  UpperCamelCase       ,  UpperCamelCase					       =  sequence[high], sequence[mid]
                  high -= 1
            else:
                  UpperCamelCase					       =  F'The elements inside the sequence must contains only {colors} values'
                  raise ValueError(_UpperCamelCase)
      return sequence


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       __magic_name__   :  str        =							input('''Enter numbers separated by commas:\n''').strip()
       __magic_name__   :  Union[str, Any]        =							[int(item.strip()) for item in user_input.split(''',''')]
       print(F'{dutch_national_flag_sort(unsorted)}')



