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

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

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

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

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

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

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

  __UpperCAmelCase:       Optional[Any]		=			[]
  __UpperCAmelCase:       Tuple		=			[]

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

      if secondary_learner is not None:
        __UpperCAmelCase:       str		=			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:       Optional[int]		=			-1
        if predicted_q < threshold:
          __UpperCAmelCase:       List[Any]		=			False

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

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

  # Generate train and test data to train and evaluate gpt2 model
  __UpperCAmelCase,			__UpperCAmelCase:       Union[str, 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'''
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate


@dataclass(frozen=snake_case_)
class 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							field(default="""language-modeling"""							,       metadata={"""include_in_asdict_even_if_is_default""": True})

				lowerCAmelCase_     			=							Features({"""text""": Value("""string""")})
				lowerCAmelCase_     			=							Features({})
				lowerCAmelCase_     			=							"text"







				@property
				def 						UpperCAmelCase_    (       self     )->		Dict[str, str]:







											'''simple docstring'''






											return {self.text_column: "text"}




