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

  # load pretrained model
  __UpperCAmelCase:					Union[str, Any]		= load_gpta("""gpt2"""     ).to(_lowercase     )
  print("""computing perplexity on objective set"""     )
  __UpperCAmelCase:					Tuple		= 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    :				int   ,    _lowercase    :				Dict=1_5   ,    _lowercase    :				Optional[int]=1_2_8   ,    _lowercase    :				str=1_0_0   ,    _lowercase    :				Optional[int]="igf_model.pt"   ,    )							->	str:
  set_seed(4_2     )

  # Load pre-trained model
  __UpperCAmelCase:					Union[str, Any]		= GPTaLMHeadModel.from_pretrained("""gpt2"""     )

  # Initialize secondary learner to use embedding weights of model
  __UpperCAmelCase:					Union[str, Any]		= SecondaryLearner(_lowercase     )

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

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

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

  __UpperCAmelCase:					Any		= []
  __UpperCAmelCase:					Optional[int]		= []

  # Compute the performance of the transformer model at the beginning
  __UpperCAmelCase:					Tuple		= 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:					List[Any]		= random.randint(0   ,    example.size(2     ) - context_len - 1     )
      __UpperCAmelCase:					Optional[int]		= example[0, 0, start : start + context_len]
      lm_optimizer.zero_grad()
      __UpperCAmelCase:					Optional[Any]		= model(_lowercase   ,    labels=_lowercase     )
      __UpperCAmelCase:					str		= True

      if secondary_learner is not None:
        __UpperCAmelCase:					Dict		= 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:					str		= -1
        if predicted_q < threshold:
          __UpperCAmelCase:					Union[str, Any]		= False

            # If we passed the filter, add the context to the batch!
      if do_backprop:
        contexts.append(np.array(context.cpu()     )     )
        __UpperCAmelCase:					Any		= 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:					int		= 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:					List[str]		= 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__					(					)							->	Dict:
  __UpperCAmelCase:					List[str]		= 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:					Optional[int]		= joblib.load("""data/IGF_values.jbl"""     )

  # Train secondary learner
  __UpperCAmelCase:					List[str]		= 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:					int		= GPTaLMHeadModel.from_pretrained("""gpt2"""     )
  set_seed(4_2     )

  # Generate train and test data to train and evaluate gpt2 model
  __UpperCAmelCase, __UpperCAmelCase:					Tuple		= 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'''






from __future__ import annotations

import time

import numpy as np

SCREAMING_SNAKE_CASE_	   =			[8, 5, 9, 7]
SCREAMING_SNAKE_CASE_	   =			[
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
SCREAMING_SNAKE_CASE_	   =			[
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]




class    a :



  """simple docstring"""







  def __init__(							self   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    ):

    '''simple docstring'''







    __UpperCAmelCase:					str		= claim_vector
    __UpperCAmelCase:					Optional[Any]		= allocated_resources_table
    __UpperCAmelCase:					List[str]		= maximum_claim_table







  def    lowercase_  (							self					):

    '''simple docstring'''







    return [
        sum(p_item[i] for p_item in self.__allocated_resources_table					)
        for i in range(len(self.__allocated_resources_table[0]					)					)
    ]







  def    lowercase_  (							self					):

    '''simple docstring'''







    return np.array(self.__claim_vector					) - np.array(
        self.__processes_resource_summation()					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    return [
        list(np.array(self.__maximum_claim_table[i]					) - np.array(snake_case_					)					)
        for i, allocated_resource in enumerate(self.__allocated_resources_table					)
    ]







  def    lowercase_  (							self					):

    '''simple docstring'''







    return {self.__need().index(snake_case_					): i for i in self.__need()}







  def    lowercase_  (							self   ,    **snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					Dict		= self.__need()
    __UpperCAmelCase:					Optional[Any]		= self.__allocated_resources_table
    __UpperCAmelCase:					int		= self.__available_resources()
    __UpperCAmelCase:					List[Any]		= self.__need_index_manager()
    for kw, val in kwargs.items():
      if kw and val is True:
        self.__pretty_data()
    print("""_""" * 50 + """\n"""					)
    while need_list:
      __UpperCAmelCase:					Any		= False
      for each_need in need_list:
        __UpperCAmelCase:					Any		= True
        for index, need in enumerate(snake_case_					):
          if need > available_resources[index]:
            __UpperCAmelCase:					Tuple		= False
            break
        if execution:
          __UpperCAmelCase:					Optional[int]		= True
          # get the original index of the process from ind_ctrl db
          for original_need_index, need_clone in need_index_manager.items():
            if each_need == need_clone:
              __UpperCAmelCase:					Tuple		= original_need_index
          print(F'''Process {process_number + 1} is executing.'''					)
          # remove the process run from stack
          need_list.remove(snake_case_					)
          # update available/freed resources stack
          __UpperCAmelCase:					Dict		= np.array(snake_case_					) + np.array(
              alloc_resources_table[process_number]					)
          print(
              """Updated available resource stack for processes: """
              + """ """.join([str(snake_case_					) for x in available_resources]					)					)
          break
      if safe:
        print("""The process is in a safe state.\n"""					)
      else:
        print("""System in unsafe state. Aborting...\n"""					)
        break







  def    lowercase_  (							self					):

    '''simple docstring'''







    print(""" """ * 9 + """Allocated Resource Table"""					)
    for item in self.__allocated_resources_table:
      print(
          F'''P{self.__allocated_resources_table.index(snake_case_					) + 1}'''
          + """ """.join(F'''{it:>8}''' for it in item					)
          + """\n"""					)
    print(""" """ * 9 + """System Resource Table"""					)
    for item in self.__maximum_claim_table:
      print(
          F'''P{self.__maximum_claim_table.index(snake_case_					) + 1}'''
          + """ """.join(F'''{it:>8}''' for it in item					)
          + """\n"""					)
    print(
        """Current Usage by Active Processes: """
        + """ """.join(str(snake_case_					) for x in self.__claim_vector					)					)
    print(
        """Initial Available Resources:       """
        + """ """.join(str(snake_case_					) for x in self.__available_resources()					)					)
    time.sleep(1					)


if __name__ == "__main__":
    import doctest

    doctest.testmod()