


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


snake_case    :   str	    =					logging.get_logger(__name__)

snake_case    :   Dict	    =					{}







class 		_snake_case  (				_snake_case							):
 SCREAMING_SNAKE_CASE__				=	'llama'
 SCREAMING_SNAKE_CASE__				=	['past_key_values']




 def __init__(		self ,  _lowerCamelCase=3_2000 ,  _lowerCamelCase=4096 ,  _lowerCamelCase=1_1008 ,  _lowerCamelCase=32 ,  _lowerCamelCase=32 ,  _lowerCamelCase=None ,  _lowerCamelCase="silu" ,  _lowerCamelCase=2048 ,  _lowerCamelCase=0.02 ,  _lowerCamelCase=1e-6 ,  _lowerCamelCase=True ,  _lowerCamelCase=0 ,  _lowerCamelCase=1 ,  _lowerCamelCase=2 ,  _lowerCamelCase=1 ,  _lowerCamelCase=False ,  _lowerCamelCase=None ,  **_lowerCamelCase ,  ):
       a						:Tuple										=	vocab_size
       a						:Optional[int]										=	max_position_embeddings
       a						:Dict										=	hidden_size
       a						:Dict										=	intermediate_size
       a						:str										=	num_hidden_layers
       a						:int										=	num_attention_heads

       # for backward compatibility
       if num_key_value_heads is None:
             a						:Optional[Any]										=	num_attention_heads

       a						:Optional[Any]										=	num_key_value_heads
       a						:Optional[int]										=	hidden_act
       a						:str										=	initializer_range
       a						:List[Any]										=	rms_norm_eps
       a						:Optional[int]										=	pretraining_tp
       a						:Any										=	use_cache
       a						:Tuple										=	rope_scaling
       self._rope_scaling_validation()

       super().__init__(
           pad_token_id=_lowerCamelCase ,  bos_token_id=_lowerCamelCase ,  eos_token_id=_lowerCamelCase ,  tie_word_embeddings=_lowerCamelCase ,  **_lowerCamelCase ,  )




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       if self.rope_scaling is None:
             return

       if not isinstance(self.rope_scaling ,  _lowerCamelCase   ) or len(self.rope_scaling   ) != 2:
             raise ValueError(
                 '''`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '''
                 F'''got {self.rope_scaling}'''   )
       a						:Dict										=	self.rope_scaling.get('''type''' ,  _lowerCamelCase   )
       a						:int										=	self.rope_scaling.get('''factor''' ,  _lowerCamelCase   )
       if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
             raise ValueError(
                 F'''`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'''   )
       if rope_scaling_factor is None or not isinstance(_lowerCamelCase ,  _lowerCamelCase   ) or rope_scaling_factor <= 1.0:
             raise ValueError(F'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'''   )









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     __lowerCamelCase						(			UpperCAmelCase_						:		Optional[int]=32			,  UpperCAmelCase_						:		Any=10			,  UpperCAmelCase_						:		Any=100			,  UpperCAmelCase_						:		List[str]=1026			,  UpperCAmelCase_						:		Optional[int]=True			,  UpperCAmelCase_						:		str="data/tokenized_stories_train_wikitext103.jbl"			,  UpperCAmelCase_						:		List[Any]="igf_context_pairs.jbl"			,  ):




      """simple docstring"""





      set_seed(3						)
      # generate train_data and objective_set
      a							,    a						:Optional[int]										=	generate_datasets(
          UpperCAmelCase_			,  UpperCAmelCase_			,  number=UpperCAmelCase_			,  min_len=1026			,  trim=UpperCAmelCase_						)
      # 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?
      a						:str										=	torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''						)

      # load pretrained model
      a						:str										=	load_gpta('''gpt2'''						).to(UpperCAmelCase_						)
      print('''computing perplexity on objective set'''						)
      a						:Dict										=	compute_perplexity(UpperCAmelCase_			,  UpperCAmelCase_			,  UpperCAmelCase_						).item()
      print('''perplexity on objective set:'''			,  UpperCAmelCase_						)

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

      # clean up, delete model and data we don't need anymore
      del model, train_data, objective_set
      torch.cuda.empty_cache()
def     __lowerCamelCase						(			UpperCAmelCase_						:		Optional[int]			,  UpperCAmelCase_						:		str=15			,  UpperCAmelCase_						:		Optional[Any]=128			,  UpperCAmelCase_						:		List[Any]=100			,  UpperCAmelCase_						:		List[str]="igf_model.pt"			,  ):




      """simple docstring"""





      set_seed(42						)

      # Load pre-trained model
      a						:Tuple										=	GPTaLMHeadModel.from_pretrained('''gpt2'''						)

      # Initialize secondary learner to use embedding weights of model
      a						:List[str]										=	SecondaryLearner(UpperCAmelCase_						)

      # Train secondary learner
      a						:List[str]										=	train_secondary_learner(
          UpperCAmelCase_			,  UpperCAmelCase_			,  max_epochs=UpperCAmelCase_			,  batch_size=UpperCAmelCase_			,  eval_freq=100			,  igf_model_path=UpperCAmelCase_			,  )

      del model, secondary_learner_train_data
      torch.cuda.empty_cache()

      return secondary_learner
def     __lowerCamelCase						(			UpperCAmelCase_						:		Any			,  UpperCAmelCase_						:		Union[str, Any]			,  UpperCAmelCase_						:		int			,  UpperCAmelCase_						:		Optional[Any]=32			,  UpperCAmelCase_						:		List[str]=1000			,  UpperCAmelCase_						:		Union[str, Any]=16			,  UpperCAmelCase_						:		Any=1.0			,  UpperCAmelCase_						:		Optional[int]=recopy_gpta			,  UpperCAmelCase_						:		Optional[Any]=None			,  UpperCAmelCase_						:		Tuple=10			,  UpperCAmelCase_						:		Any="gpt2_finetuned.pt"			,  ):




      """simple docstring"""





      a						:Optional[Any]										=	torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''						)
      a						:Optional[Any]										=	RandomSampler(UpperCAmelCase_						)
      a						:Union[str, Any]										=	DataLoader(UpperCAmelCase_			,  sampler=UpperCAmelCase_						)

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

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

      a						:Optional[Any]										=	[]
      a						:Tuple										=	[]

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

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

                        # 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:
                              a						:List[str]										=	-1
                        if predicted_q < threshold:
                              a						:Tuple										=	False

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

                              print('''Test perplexity, step'''			,  UpperCAmelCase_			,  ''':'''			,  UpperCAmelCase_						)
            # 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()			,  UpperCAmelCase_						)
      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     __lowerCamelCase						(			):




      """simple docstring"""





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

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

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

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

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

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

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

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

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

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

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

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

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

      # Load train data for secondary learner
      a						:Union[str, Any]										=	joblib.load('''data/IGF_values.jbl'''						)

      # Train secondary learner
      a						:Any										=	training_secondary_learner(
          UpperCAmelCase_			,  secondary_learner_max_epochs=15			,  secondary_learner_batch_size=128			,  eval_freq=100			,  igf_model_path='''igf_model.pt'''			,  )

      # load pretrained gpt2 model
      a						:Any										=	GPTaLMHeadModel.from_pretrained('''gpt2'''						)
      set_seed(42						)

      # Generate train and test data to train and evaluate gpt2 model
      a							,    a						:Union[str, Any]										=	generate_datasets(
          context_len=32			,  file='''data/tokenized_stories_train_wikitext103.jbl'''			,  number=100			,  min_len=1026			,  trim=UpperCAmelCase_						)

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


if __name__ == "__main__":
     main()





