



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    A_  (							a=3_2					,   a=1_0					,   a=1_0_0					,   a=1_0_2_6					,   a=True					,   a="data/tokenized_stories_train_wikitext103.jbl"					,   a="igf_context_pairs.jbl"					,   ):

      """simple docstring"""



      set_seed(3  )
      # generate train_data and objective_set
      SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     generate_datasets(
          a					,   a					,   number=a					,   min_len=1_0_2_6					,   trim=a  )
      # 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?
      SCREAMING_SNAKE_CASE_				:  str           =     torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'  )

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

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

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

def    A_  (							a					,   a=1_5					,   a=1_2_8					,   a=1_0_0					,   a="igf_model.pt"					,   ):

      """simple docstring"""



      set_seed(4_2  )

      # Load pre-trained model
      SCREAMING_SNAKE_CASE_				:  int           =     GPTaLMHeadModel.from_pretrained('gpt2'  )

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

      # Train secondary learner
      SCREAMING_SNAKE_CASE_				:  Any           =     train_secondary_learner(
          a					,   a					,   max_epochs=a					,   batch_size=a					,   eval_freq=1_0_0					,   igf_model_path=a					,   )

      del model, secondary_learner_train_data
      torch.cuda.empty_cache()

      return secondary_learner

def    A_  (							a					,   a					,   a					,   a=3_2					,   a=1_0_0_0					,   a=1_6					,   a=1.0					,   a=recopy_gpta					,   a=None					,   a=1_0					,   a="gpt2_finetuned.pt"					,   ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  Dict           =     torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'  )
      SCREAMING_SNAKE_CASE_				:  List[str]           =     RandomSampler(a  )
      SCREAMING_SNAKE_CASE_				:  Dict           =     DataLoader(a					,   sampler=a  )

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

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

      SCREAMING_SNAKE_CASE_				:  List[Any]           =     []
      SCREAMING_SNAKE_CASE_				:  Dict           =     []

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

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

                        # 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:
                              SCREAMING_SNAKE_CASE_				:  Any           =     -1
                        if predicted_q < threshold:
                              SCREAMING_SNAKE_CASE_				:  Tuple           =     False

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

                              print('Test perplexity, step'					,   a					,   ':'					,   a  )
            # 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()					,   a  )
      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    A_  (							):

      """simple docstring"""



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

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

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

      parser.add_argument(
          '--context_len'					,   default=3_2					,   type=a					,   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=a					,   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=a					,   help='secondary model evaluation is triggered at eval_freq'  )

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

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

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

      parser.add_argument(
          '--eval_interval'					,   default=1_0					,   type=a					,   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=a					,   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=a					,   help='The minimum length of the article to be used as objective set'  )

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

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

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

      parser.add_argument(
          '--recopy_model'					,   default=a					,   type=a					,   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=a					,   data_file='data/tokenized_stories_train_wikitext103.jbl'					,   igf_data_file='igf_context_pairs.jbl'					,   )

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

      # Train secondary learner
      SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     training_secondary_learner(
          a					,   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
      SCREAMING_SNAKE_CASE_				:  int           =     GPTaLMHeadModel.from_pretrained('gpt2'  )
      set_seed(4_2  )

      # Generate train and test data to train and evaluate gpt2 model
      SCREAMING_SNAKE_CASE_				:  List[str]           =     generate_datasets(
          context_len=3_2					,   file='data/tokenized_stories_train_wikitext103.jbl'					,   number=1_0_0					,   min_len=1_0_2_6					,   trim=a  )

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


if __name__ == "__main__":
    main()









import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=__magic_name__)
class  _A		(  __magic_name__):
  SCREAMING_SNAKE_CASE    :      str       =  field(default='''automatic-speech-recognition'''   ,     metadata={'''include_in_asdict_even_if_is_default''': True})
  SCREAMING_SNAKE_CASE    :      ClassVar[Features]       =  Features({'''audio''': Audio()})
  SCREAMING_SNAKE_CASE    :      ClassVar[Features]       =  Features({'''transcription''': Value('''string''')})
  SCREAMING_SNAKE_CASE    :      str       =  "audio"
  SCREAMING_SNAKE_CASE    :      str       =  "transcription"

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        if self.audio_column not in features:
              raise ValueError(f"Column {self.audio_column} is not present in features."					)
        if not isinstance(features[self.audio_column] ,					_SCREAMING_SNAKE_CASE					):
              raise ValueError(f"Column {self.audio_column} is not an Audio type."					)
        SCREAMING_SNAKE_CASE_				:  int           =     copy.deepcopy(self					)
        SCREAMING_SNAKE_CASE_				:  int           =     self.input_schema.copy()
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     features[self.audio_column]
        SCREAMING_SNAKE_CASE_				:  List[str]           =     input_schema
        return task_template



  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return {self.audio_column: "audio", self.transcription_column: "transcription"}






