






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 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase=32 ,   __lowerCAmelCase=10 ,   __lowerCAmelCase=100 ,   __lowerCAmelCase=1026 ,   __lowerCAmelCase=True ,   __lowerCAmelCase="data/tokenized_stories_train_wikitext103.jbl" ,   __lowerCAmelCase="igf_context_pairs.jbl" ,   )			->     Any:
     set_seed(3      )
     # generate train_data and objective_set
     UpperCamelCase__    ,					UpperCamelCase__      :       List[Any]	    =  generate_datasets(
         __lowerCAmelCase ,   __lowerCAmelCase ,   number=__lowerCAmelCase ,   min_len=1026 ,   trim=__lowerCAmelCase      )
     # 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__      :       Tuple	    =  torch.device("cuda:0" if torch.cuda.is_available() else "cpu"      )

     # load pretrained model
     UpperCamelCase__      :       Dict	    =  load_gpta("gpt2"      ).to(__lowerCAmelCase      )
     print("computing perplexity on objective set"      )
     UpperCamelCase__      :       int	    =  compute_perplexity(__lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase      ).item()
     print("perplexity on objective set:" ,   __lowerCAmelCase      )

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

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







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase=15 ,   __lowerCAmelCase=128 ,   __lowerCAmelCase=100 ,   __lowerCAmelCase="igf_model.pt" ,   )			->     List[Any]:
     set_seed(42      )

     # Load pre-trained model
     UpperCamelCase__      :       List[Any]	    =  GPTaLMHeadModel.from_pretrained("gpt2"      )

     # Initialize secondary learner to use embedding weights of model
     UpperCamelCase__      :       Optional[int]	    =  SecondaryLearner(__lowerCAmelCase      )

     # Train secondary learner
     UpperCamelCase__      :       Any	    =  train_secondary_learner(
         __lowerCAmelCase ,   __lowerCAmelCase ,   max_epochs=__lowerCAmelCase ,   batch_size=__lowerCAmelCase ,   eval_freq=100 ,   igf_model_path=__lowerCAmelCase ,   )

     del model, secondary_learner_train_data
     torch.cuda.empty_cache()

     return secondary_learner







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase=32 ,   __lowerCAmelCase=1000 ,   __lowerCAmelCase=16 ,   __lowerCAmelCase=1.0 ,   __lowerCAmelCase=recopy_gpta ,   __lowerCAmelCase=None ,   __lowerCAmelCase=10 ,   __lowerCAmelCase="gpt2_finetuned.pt" ,   )			->     Optional[int]:
     UpperCamelCase__      :       Tuple	    =  torch.device("cuda:0" if torch.cuda.is_available() else "cpu"      )
     UpperCamelCase__      :       Union[str, Any]	    =  RandomSampler(__lowerCAmelCase      )
     UpperCamelCase__      :       Dict	    =  DataLoader(__lowerCAmelCase ,   sampler=__lowerCAmelCase      )

     UpperCamelCase__      :       Tuple	    =  max_steps // (len(__lowerCAmelCase      )) + 1
     UpperCamelCase__      :       List[str]	    =  0
     UpperCamelCase__      :       List[str]	    =  torch.zeros((1, context_len) ,   dtype=torch.long ,   device=__lowerCAmelCase      )
     UpperCamelCase__    ,					UpperCamelCase__    ,					UpperCamelCase__      :       List[str]	    =  recopy_model(__lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase      )

     model.train()
     if secondary_learner is not None:
          secondary_learner.to(__lowerCAmelCase      )
          secondary_learner.eval()
     UpperCamelCase__      :       str	    =  []
     UpperCamelCase__      :       Union[str, Any]	    =  0

     UpperCamelCase__      :       List[Any]	    =  []
     UpperCamelCase__      :       Union[str, Any]	    =  []

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

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

                    # 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__      :       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__      :       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__      :       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__      :       Union[str, Any]	    =  compute_perplexity(__lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase      )
                         test_perps.append(__lowerCAmelCase      )

                         print("Test perplexity, step" ,   __lowerCAmelCase ,   ":" ,   __lowerCAmelCase      )
            # 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() ,   __lowerCAmelCase      )
     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 		SCREAMING_SNAKE_CASE							( )			->     List[Any]:
     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=__lowerCAmelCase ,   type=__lowerCAmelCase ,   required=__lowerCAmelCase ,   help="The input data dir. Should contain data files for WikiText." ,   )
     parser.add_argument(
         "--model_name_or_path" ,   default=__lowerCAmelCase ,   type=__lowerCAmelCase ,   required=__lowerCAmelCase ,   help="Path to pretrained model or model identifier from huggingface.co/models" ,   )
     parser.add_argument(
         "--data_file" ,   type=__lowerCAmelCase ,   default=__lowerCAmelCase ,   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=__lowerCAmelCase ,   default=__lowerCAmelCase ,   help="A jbl file containing the context and information gain pairs to train secondary learner." ,   )

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

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

     parser.add_argument(
         "--context_len" ,   default=32 ,   type=__lowerCAmelCase ,   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=100 ,   type=__lowerCAmelCase ,   help="number of articles that are long enough to be used as our objective set" ,   )
     parser.add_argument(
         "--eval_freq" ,   default=100 ,   type=__lowerCAmelCase ,   help="secondary model evaluation is triggered at eval_freq"      )

     parser.add_argument("--max_steps" ,   default=1000 ,   type=__lowerCAmelCase ,   help="To calculate training epochs"      )

     parser.add_argument(
         "--secondary_learner_batch_size" ,   default=128 ,   type=__lowerCAmelCase ,   help="batch size of training data for secondary learner" ,   )

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

     parser.add_argument(
         "--eval_interval" ,   default=10 ,   type=__lowerCAmelCase ,   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=100 ,   type=__lowerCAmelCase ,   help="The number of examples split to be used as objective_set/test_data"      )

     parser.add_argument(
         "--min_len" ,   default=1026 ,   type=__lowerCAmelCase ,   help="The minimum length of the article to be used as objective set"      )

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

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

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

     parser.add_argument(
         "--recopy_model" ,   default=__lowerCAmelCase ,   type=__lowerCAmelCase ,   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=100 ,   min_len=1026 ,   trim=__lowerCAmelCase ,   data_file="data/tokenized_stories_train_wikitext103.jbl" ,   igf_data_file="igf_context_pairs.jbl" ,   )

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

     # Train secondary learner
     UpperCamelCase__      :       int	    =  training_secondary_learner(
         __lowerCAmelCase ,   secondary_learner_max_epochs=15 ,   secondary_learner_batch_size=128 ,   eval_freq=100 ,   igf_model_path="igf_model.pt" ,   )

     # load pretrained gpt2 model
     UpperCamelCase__      :       Union[str, Any]	    =  GPTaLMHeadModel.from_pretrained("gpt2"      )
     set_seed(42      )

     # Generate train and test data to train and evaluate gpt2 model
     UpperCamelCase__    ,					UpperCamelCase__      :       Any	    =  generate_datasets(
         context_len=32 ,   file="data/tokenized_stories_train_wikitext103.jbl" ,   number=100 ,   min_len=1026 ,   trim=__lowerCAmelCase      )

     # fine-tuning of the gpt2 model using igf (Information Gain Filtration)
     finetune(
         __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   context_len=32 ,   max_steps=1000 ,   batch_size=16 ,   threshold=1.0 ,   recopy_model=__lowerCAmelCase ,   secondary_learner=__lowerCAmelCase ,   eval_interval=10 ,   finetuned_model_name="gpt2_finetuned.pt" ,   )


if __name__ == "__main__":
 main()







from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase     :							Dict    =logging.get_logger(__name__)





class       __a  (   A__	):
 _lowerCAmelCase	:	Optional[int]				=			'''timm_backbone'''







 def __init__(		self :       Dict							,       SCREAMING_SNAKE_CASE :       Dict=None							,       SCREAMING_SNAKE_CASE :       List[Any]=3							,       SCREAMING_SNAKE_CASE :       List[Any]=True							,       SCREAMING_SNAKE_CASE :       Dict=True							,       SCREAMING_SNAKE_CASE :       Union[str, Any]=None							,       **SCREAMING_SNAKE_CASE :       int							,       ):


      '''simple docstring'''






      super().__init__(**SCREAMING_SNAKE_CASE						)
      UpperCamelCase__      :       Union[str, Any]	    =  backbone
      UpperCamelCase__      :       Dict	    =  num_channels
      UpperCamelCase__      :       str	    =  features_only
      UpperCamelCase__      :       Dict	    =  use_pretrained_backbone
      UpperCamelCase__      :       Tuple	    =  True
      UpperCamelCase__      :       List[Any]	    =  out_indices if out_indices is not None else (-1,)