'''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  UpperCamelCase__					(					_lowercase    :				Optional[Any]=3_2   ,    _lowercase    :				Tuple=1_0   ,    _lowercase    :				List[Any]=1_0_0   ,    _lowercase    :				Optional[Any]=1_0_2_6   ,    _lowercase    :				Any=True   ,    _lowercase    :				Optional[int]="data/tokenized_stories_train_wikitext103.jbl"   ,    _lowercase    :				Dict="igf_context_pairs.jbl"   ,    )							->	Any:
  set_seed(3     )
  # generate train_data and objective_set
  __UpperCAmelCase, __UpperCAmelCase:					Optional[Any]		= generate_datasets(
      _lowercase   ,    _lowercase   ,    number=_lowercase   ,    min_len=1_0_2_6   ,    trim=_lowercase     )
  # 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:					Optional[int]		= torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""     )

  # load pretrained model
  __UpperCAmelCase:					Optional[Any]		= load_gpta("""gpt2"""     ).to(_lowercase     )
  print("""computing perplexity on objective set"""     )
  __UpperCAmelCase:					Union[str, Any]		= compute_perplexity(_lowercase   ,    _lowercase   ,    _lowercase     ).item()
  print("""perplexity on objective set:"""   ,    _lowercase     )

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

  # clean up, delete model and data we don't need anymore
  del model, train_data, objective_set
  torch.cuda.empty_cache()






def  UpperCamelCase__					(					_lowercase    :				Any   ,    _lowercase    :				Dict=1_5   ,    _lowercase    :				Dict=1_2_8   ,    _lowercase    :				Optional[int]=1_0_0   ,    _lowercase    :				Optional[int]="igf_model.pt"   ,    )							->	Optional[Any]:
  set_seed(4_2     )

  # Load pre-trained model
  __UpperCAmelCase:					Tuple		= GPTaLMHeadModel.from_pretrained("""gpt2"""     )

  # Initialize secondary learner to use embedding weights of model
  __UpperCAmelCase:					int		= SecondaryLearner(_lowercase     )

  # Train secondary learner
  __UpperCAmelCase:					Tuple		= train_secondary_learner(
      _lowercase   ,    _lowercase   ,    max_epochs=_lowercase   ,    batch_size=_lowercase   ,    eval_freq=1_0_0   ,    igf_model_path=_lowercase   ,    )

  del model, secondary_learner_train_data
  torch.cuda.empty_cache()

  return secondary_learner






def  UpperCamelCase__					(					_lowercase    :				Optional[Any]   ,    _lowercase    :				Union[str, Any]   ,    _lowercase    :				List[Any]   ,    _lowercase    :				Union[str, Any]=3_2   ,    _lowercase    :				Optional[Any]=1_0_0_0   ,    _lowercase    :				Dict=1_6   ,    _lowercase    :				Union[str, Any]=1.0   ,    _lowercase    :				Dict=recopy_gpta   ,    _lowercase    :				Optional[int]=None   ,    _lowercase    :				List[Any]=1_0   ,    _lowercase    :				List[Any]="gpt2_finetuned.pt"   ,    )							->	List[Any]:
  __UpperCAmelCase:					Optional[int]		= torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""     )
  __UpperCAmelCase:					Any		= RandomSampler(_lowercase     )
  __UpperCAmelCase:					Any		= DataLoader(_lowercase   ,    sampler=_lowercase     )

  __UpperCAmelCase:					Optional[int]		= max_steps // (len(_lowercase     )) + 1
  __UpperCAmelCase:					List[Any]		= 0
  __UpperCAmelCase:					Dict		= torch.zeros((1, context_len)   ,    dtype=torch.long   ,    device=_lowercase     )
  __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase:					List[Any]		= recopy_model(_lowercase   ,    _lowercase   ,    _lowercase     )

  model.train()
  if secondary_learner is not None:
    secondary_learner.to(_lowercase     )
    secondary_learner.eval()
  __UpperCAmelCase:					str		= []
  __UpperCAmelCase:					Any		= 0

  __UpperCAmelCase:					Union[str, Any]		= []
  __UpperCAmelCase:					str		= []

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

      if secondary_learner is not None:
        __UpperCAmelCase:					Union[str, Any]		= secondary_learner.forward(
            torch.tensor(_lowercase   ,    dtype=torch.long   ,    device=_lowercase     ).unsqueeze(0     )     )[0].item()
        observed_qs.append(float(_lowercase     )     )

        # 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 == 1_0:
          __UpperCAmelCase:					List[str]		= -1
        if predicted_q < threshold:
          __UpperCAmelCase:					List[str]		= False

            # If we passed the filter, add the context to the batch!
      if do_backprop:
        contexts.append(np.array(context.cpu()     )     )
        __UpperCAmelCase:					Optional[int]		= 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:					Dict		= 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:					int		= compute_perplexity(_lowercase   ,    _lowercase   ,    _lowercase     )
          test_perps.append(_lowercase     )

          print("""Test perplexity, step"""   ,    _lowercase   ,    """:"""   ,    _lowercase     )
            # Break out of the loop after 60 batches
      if max_steps > 0 and global_step > 6_0:
        break
    if max_steps > 0 and global_step > 6_0:
      break

    # save finetuned transformer model
  torch.save(model.state_dict()   ,    _lowercase     )
  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  UpperCamelCase__					(					)							->	Optional[Any]:
  __UpperCAmelCase:					Union[str, Any]		= argparse.ArgumentParser(description="""Fine-tune a transformer model with IGF on a language modeling task"""     )

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

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

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

  parser.add_argument(
      """--context_len"""   ,    default=3_2   ,    type=_lowercase   ,    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_0_0   ,    type=_lowercase   ,    help="""number of articles that are long enough to be used as our objective set"""   ,    )
  parser.add_argument(
      """--eval_freq"""   ,    default=1_0_0   ,    type=_lowercase   ,    help="""secondary model evaluation is triggered at eval_freq"""     )

  parser.add_argument("""--max_steps"""   ,    default=1_0_0_0   ,    type=_lowercase   ,    help="""To calculate training epochs"""     )

  parser.add_argument(
      """--secondary_learner_batch_size"""   ,    default=1_2_8   ,    type=_lowercase   ,    help="""batch size of training data for secondary learner"""   ,    )

  parser.add_argument(
      """--batch_size"""   ,    default=1_6   ,    type=_lowercase   ,    help="""batch size of training data of language model(gpt2) """     )

  parser.add_argument(
      """--eval_interval"""   ,    default=1_0   ,    type=_lowercase   ,    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_0_0   ,    type=_lowercase   ,    help="""The number of examples split to be used as objective_set/test_data"""     )

  parser.add_argument(
      """--min_len"""   ,    default=1_0_2_6   ,    type=_lowercase   ,    help="""The minimum length of the article to be used as objective set"""     )

  parser.add_argument(
      """--secondary_learner_max_epochs"""   ,    default=1_5   ,    type=_lowercase   ,    help="""number of epochs to train secondary learner"""     )

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

  parser.add_argument(
      """--threshold"""   ,    default=1.0   ,    type=_lowercase   ,    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=_lowercase   ,    help="""finetuned_model_name"""     )

  parser.add_argument(
      """--recopy_model"""   ,    default=_lowercase   ,    type=_lowercase   ,    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=3_2   ,    max_steps=1_0   ,    size_objective_set=1_0_0   ,    min_len=1_0_2_6   ,    trim=_lowercase   ,    data_file="""data/tokenized_stories_train_wikitext103.jbl"""   ,    igf_data_file="""igf_context_pairs.jbl"""   ,    )

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

  # Train secondary learner
  __UpperCAmelCase:					Optional[int]		= training_secondary_learner(
      _lowercase   ,    secondary_learner_max_epochs=1_5   ,    secondary_learner_batch_size=1_2_8   ,    eval_freq=1_0_0   ,    igf_model_path="""igf_model.pt"""   ,    )

  # load pretrained gpt2 model
  __UpperCAmelCase:					List[str]		= GPTaLMHeadModel.from_pretrained("""gpt2"""     )
  set_seed(4_2     )

  # Generate train and test data to train and evaluate gpt2 model
  __UpperCAmelCase, __UpperCAmelCase:					Optional[Any]		= generate_datasets(
      context_len=3_2   ,    file="""data/tokenized_stories_train_wikitext103.jbl"""   ,    number=1_0_0   ,    min_len=1_0_2_6   ,    trim=_lowercase     )

  # fine-tuning of the gpt2 model using igf (Information Gain Filtration)
  finetune(
      _lowercase   ,    _lowercase   ,    _lowercase   ,    context_len=3_2   ,    max_steps=1_0_0_0   ,    batch_size=1_6   ,    threshold=1.0   ,    recopy_model=_lowercase   ,    secondary_learner=_lowercase   ,    eval_interval=1_0   ,    finetuned_model_name="""gpt2_finetuned.pt"""   ,    )


if __name__ == "__main__":
    main()
'''simple docstring'''






import itertools
import math






def  UpperCamelCase__					(					_lowercase    :				int     )							->	bool:

  if 1 < number < 4:
    # 2 and 3 are primes
    return True
  elif number < 2 or number % 2 == 0 or number % 3 == 0:
    # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
    return False

  # All primes number are in format of 6k +/- 1
  for i in range(5   ,    int(math.sqrt(_lowercase     ) + 1     )   ,    6     ):
    if number % i == 0 or number % (i + 2) == 0:
      return False
  return True






def  UpperCamelCase__					(					)							->	Optional[int]:
  __UpperCAmelCase:					Union[str, Any]		= 2
  while True:
    if is_prime(_lowercase     ):
      yield num
    num += 1






def  UpperCamelCase__					(					_lowercase    :				int = 1_0_0_0_1     )							->	int:
  return next(itertools.islice(prime_generator()   ,    nth - 1   ,    _lowercase     )     )


if __name__ == "__main__":
    print(F"""{solution() = }""")