





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    __lowercase   (      _SCREAMING_SNAKE_CASE=32 ,						_SCREAMING_SNAKE_CASE=10 ,						_SCREAMING_SNAKE_CASE=1_00 ,						_SCREAMING_SNAKE_CASE=10_26 ,						_SCREAMING_SNAKE_CASE=True ,						_SCREAMING_SNAKE_CASE="data/tokenized_stories_train_wikitext103.jbl" ,						_SCREAMING_SNAKE_CASE="igf_context_pairs.jbl" ,						) ->   int:


      '''simple docstring'''


      set_seed(3	)
      # generate train_data and objective_set
      SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE		=						generate_datasets(
          _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						number=_SCREAMING_SNAKE_CASE ,						min_len=10_26 ,						trim=_SCREAMING_SNAKE_CASE	)
      # 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		=						torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""	)

      # load pretrained model
      SCREAMING_SNAKE_CASE		=						load_gpta("""gpt2"""	).to(_SCREAMING_SNAKE_CASE	)
      print("""computing perplexity on objective set"""	)
      SCREAMING_SNAKE_CASE		=						compute_perplexity(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	).item()
      print("""perplexity on objective set:""" ,						_SCREAMING_SNAKE_CASE	)

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

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



def    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE=15 ,						_SCREAMING_SNAKE_CASE=1_28 ,						_SCREAMING_SNAKE_CASE=1_00 ,						_SCREAMING_SNAKE_CASE="igf_model.pt" ,						) ->   Dict:


      '''simple docstring'''


      set_seed(42	)

      # Load pre-trained model
      SCREAMING_SNAKE_CASE		=						GPTaLMHeadModel.from_pretrained("""gpt2"""	)

      # Initialize secondary learner to use embedding weights of model
      SCREAMING_SNAKE_CASE		=						SecondaryLearner(_SCREAMING_SNAKE_CASE	)

      # Train secondary learner
      SCREAMING_SNAKE_CASE		=						train_secondary_learner(
          _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						max_epochs=_SCREAMING_SNAKE_CASE ,						batch_size=_SCREAMING_SNAKE_CASE ,						eval_freq=1_00 ,						igf_model_path=_SCREAMING_SNAKE_CASE ,						)

      del model, secondary_learner_train_data
      torch.cuda.empty_cache()

      return secondary_learner



def    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE=32 ,						_SCREAMING_SNAKE_CASE=10_00 ,						_SCREAMING_SNAKE_CASE=16 ,						_SCREAMING_SNAKE_CASE=1.0 ,						_SCREAMING_SNAKE_CASE=recopy_gpta ,						_SCREAMING_SNAKE_CASE=None ,						_SCREAMING_SNAKE_CASE=10 ,						_SCREAMING_SNAKE_CASE="gpt2_finetuned.pt" ,						) ->   str:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""	)
      SCREAMING_SNAKE_CASE		=						RandomSampler(_SCREAMING_SNAKE_CASE	)
      SCREAMING_SNAKE_CASE		=						DataLoader(_SCREAMING_SNAKE_CASE ,						sampler=_SCREAMING_SNAKE_CASE	)

      SCREAMING_SNAKE_CASE		=						max_steps // (len(_SCREAMING_SNAKE_CASE	)) + 1
      SCREAMING_SNAKE_CASE		=						0
      SCREAMING_SNAKE_CASE		=						torch.zeros((1, context_len) ,						dtype=torch.long ,						device=_SCREAMING_SNAKE_CASE	)
      SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE		=						recopy_model(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	)

      model.train()
      if secondary_learner is not None:
            secondary_learner.to(_SCREAMING_SNAKE_CASE	)
            secondary_learner.eval()
      SCREAMING_SNAKE_CASE		=						[]
      SCREAMING_SNAKE_CASE		=						0

      SCREAMING_SNAKE_CASE		=						[]
      SCREAMING_SNAKE_CASE		=						[]

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

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

                        # 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:
                              SCREAMING_SNAKE_CASE		=						-1
                        if predicted_q < threshold:
                              SCREAMING_SNAKE_CASE		=						False

            # If we passed the filter, add the context to the batch!
                  if do_backprop:
                        contexts.append(np.array(context.cpu()	)	)
                        SCREAMING_SNAKE_CASE		=						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		=						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		=						compute_perplexity(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	)
                              test_perps.append(_SCREAMING_SNAKE_CASE	)

                              print("""Test perplexity, step""" ,						_SCREAMING_SNAKE_CASE ,						""":""" ,						_SCREAMING_SNAKE_CASE	)
            # 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() ,						_SCREAMING_SNAKE_CASE	)
      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    __lowercase   (      ) ->   Dict:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						argparse.ArgumentParser(description="""Fine-tune a transformer model with IGF on a language modeling task"""	)

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

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

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

      parser.add_argument(
          """--context_len""" ,						default=32 ,						type=_SCREAMING_SNAKE_CASE ,						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_00 ,						type=_SCREAMING_SNAKE_CASE ,						help="""number of articles that are long enough to be used as our objective set""" ,						)
      parser.add_argument(
          """--eval_freq""" ,						default=1_00 ,						type=_SCREAMING_SNAKE_CASE ,						help="""secondary model evaluation is triggered at eval_freq"""	)

      parser.add_argument("""--max_steps""" ,						default=10_00 ,						type=_SCREAMING_SNAKE_CASE ,						help="""To calculate training epochs"""	)

      parser.add_argument(
          """--secondary_learner_batch_size""" ,						default=1_28 ,						type=_SCREAMING_SNAKE_CASE ,						help="""batch size of training data for secondary learner""" ,						)

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

      parser.add_argument(
          """--eval_interval""" ,						default=10 ,						type=_SCREAMING_SNAKE_CASE ,						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_00 ,						type=_SCREAMING_SNAKE_CASE ,						help="""The number of examples split to be used as objective_set/test_data"""	)

      parser.add_argument(
          """--min_len""" ,						default=10_26 ,						type=_SCREAMING_SNAKE_CASE ,						help="""The minimum length of the article to be used as objective set"""	)

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

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

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

      parser.add_argument(
          """--recopy_model""" ,						default=_SCREAMING_SNAKE_CASE ,						type=_SCREAMING_SNAKE_CASE ,						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=1_00 ,						min_len=10_26 ,						trim=_SCREAMING_SNAKE_CASE ,						data_file="""data/tokenized_stories_train_wikitext103.jbl""" ,						igf_data_file="""igf_context_pairs.jbl""" ,						)

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

      # Train secondary learner
      SCREAMING_SNAKE_CASE		=						training_secondary_learner(
          _SCREAMING_SNAKE_CASE ,						secondary_learner_max_epochs=15 ,						secondary_learner_batch_size=1_28 ,						eval_freq=1_00 ,						igf_model_path="""igf_model.pt""" ,						)

      # load pretrained gpt2 model
      SCREAMING_SNAKE_CASE		=						GPTaLMHeadModel.from_pretrained("""gpt2"""	)
      set_seed(42	)

      # Generate train and test data to train and evaluate gpt2 model
      SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE		=						generate_datasets(
          context_len=32 ,						file="""data/tokenized_stories_train_wikitext103.jbl""" ,						number=1_00 ,						min_len=10_26 ,						trim=_SCREAMING_SNAKE_CASE	)

      # fine-tuning of the gpt2 model using igf (Information Gain Filtration)
      finetune(
          _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						context_len=32 ,						max_steps=10_00 ,						batch_size=16 ,						threshold=1.0 ,						recopy_model=_SCREAMING_SNAKE_CASE ,						secondary_learner=_SCREAMING_SNAKE_CASE ,						eval_interval=10 ,						finetuned_model_name="""gpt2_finetuned.pt""" ,						)


if __name__ == "__main__":
      main()









import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


SCREAMING_SNAKE_CASE_						   =       logging.get_logger(__name__)



def    __lowercase   (      _SCREAMING_SNAKE_CASE=None ,						_SCREAMING_SNAKE_CASE=None	) ->   List[str]:


      '''simple docstring'''


      return field(default_factory=lambda: default ,						metadata=_SCREAMING_SNAKE_CASE	)



@dataclass
class 		UpperCamelCase__ :



     '''simple docstring'''

     __snake_case    :	List[str]			=			list_field(
         default=[]							,      metadata={
             "help": (
                 "Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"
                 " of all available models"
             )
         }							,      )

     __snake_case    :	List[int]			=			list_field(
         default=[8]							,      metadata={"help": "List of batch sizes for which memory and time performance will be evaluated"}   )

     __snake_case    :	List[int]			=			list_field(
         default=[8, 32, 128, 512]							,      metadata={"help": "List of sequence lengths for which memory and time performance will be evaluated"}							,      )

     __snake_case    :	bool			=			field(
         default=lowerCAmelCase_							,      metadata={"help": "Whether to benchmark inference of model. Inference can be disabled via --no-inference."}							,      )
     __snake_case    :	bool			=			field(
         default=lowerCAmelCase_							,      metadata={"help": "Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."}							,      )
     __snake_case    :	bool			=			field(
         default=lowerCAmelCase_							,      metadata={"help": "Whether to run on available tpu devices. TPU can be disabled via --no-tpu."}   )
     __snake_case    :	bool			=			field(default=lowerCAmelCase_							,      metadata={"help": "Use FP16 to accelerate inference."}   )
     __snake_case    :	bool			=			field(default=lowerCAmelCase_							,      metadata={"help": "Benchmark training of model"}   )
     __snake_case    :	bool			=			field(default=lowerCAmelCase_							,      metadata={"help": "Verbose memory tracing"}   )
     __snake_case    :	bool			=			field(
         default=lowerCAmelCase_							,      metadata={"help": "Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."}							,      )
     __snake_case    :	bool			=			field(
         default=lowerCAmelCase_							,      metadata={
             "help": "Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"
         }							,      )
     __snake_case    :	bool			=			field(default=lowerCAmelCase_							,      metadata={"help": "Trace memory line by line"}   )
     __snake_case    :	bool			=			field(default=lowerCAmelCase_							,      metadata={"help": "Save result to a CSV file"}   )
     __snake_case    :	bool			=			field(default=lowerCAmelCase_							,      metadata={"help": "Save all print statements in a log file"}   )
     __snake_case    :	bool			=			field(default=lowerCAmelCase_							,      metadata={"help": "Whether to print environment information"}   )
     __snake_case    :	bool			=			field(
         default=lowerCAmelCase_							,      metadata={
             "help": (
                 "Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"
                 " multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"
                 " for debugging / testing and on TPU."
             )
         }							,      )
     __snake_case    :	str			=			field(
         default=F"inference_time_{round(time()   )}.csv"							,      metadata={"help": "CSV filename used if saving time results to csv."}							,      )
     __snake_case    :	str			=			field(
         default=F"inference_memory_{round(time()   )}.csv"							,      metadata={"help": "CSV filename used if saving memory results to csv."}							,      )
     __snake_case    :	str			=			field(
         default=F"train_time_{round(time()   )}.csv"							,      metadata={"help": "CSV filename used if saving time results to csv for training."}							,      )
     __snake_case    :	str			=			field(
         default=F"train_memory_{round(time()   )}.csv"							,      metadata={"help": "CSV filename used if saving memory results to csv for training."}							,      )
     __snake_case    :	str			=			field(
         default=F"env_info_{round(time()   )}.csv"							,      metadata={"help": "CSV filename used if saving environment information."}							,      )
     __snake_case    :	str			=			field(
         default=F"log_{round(time()   )}.csv"							,      metadata={"help": "Log filename used if print statements are saved in log."}							,      )
     __snake_case    :	int			=			field(default=3							,      metadata={"help": "Times an experiment will be run."}   )
     __snake_case    :	bool			=			field(
         default=lowerCAmelCase_							,      metadata={
             "help": (
                 "Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"
                 " model weights."
             )
         }							,      )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Union[str, Any]       )     ->		Dict:




           '''simple docstring'''


           warnings.warn(
               F"""The class {self.__class__} is deprecated. Hugging Face Benchmarking utils"""
               """ are deprecated in general and it is advised to use external Benchmarking libraries """
               """ to benchmark Transformer models."""						,lowerCamelCase__						,)






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[int]       )     ->		Any:




           '''simple docstring'''


           return json.dumps(dataclasses.asdict(self       )						,indent=2       )






     @property
     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[Any]       )     ->		List[str]:




           '''simple docstring'''


           if len(self.models       ) <= 0:
                 raise ValueError(
                     """Please make sure you provide at least one model name / model identifier, *e.g.* `--models"""
                     """ bert-base-cased` or `args.models = ['bert-base-cased']."""       )
           return self.models






     @property
     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[int]       )     ->		int:




           '''simple docstring'''


           if not self.multi_process:
                 return False
           elif self.is_tpu:
                 logger.info("""Multiprocessing is currently not possible on TPU."""       )
                 return False
           else:
                 return True


