



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      (  snake_case__			:       Optional[Any]=32			,	snake_case__			:       Optional[int]=10			,	snake_case__			:       Optional[int]=100			,	snake_case__			:       Union[str, Any]=1026			,	snake_case__			:       Any=True			,	snake_case__			:       List[str]="data/tokenized_stories_train_wikitext103.jbl"			,	snake_case__			:       Tuple="igf_context_pairs.jbl"			,	)      ->	str:
   set_seed(3      )
   # generate train_data and objective_set
   UpperCamelCase       :    Union[str, Any]		      =      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?
   UpperCamelCase       :    Optional[Any]		      =      torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'      )

   # load pretrained model
   UpperCamelCase       :    Tuple		      =      load_gpta('gpt2'      ).to(UpperCamelCase_      )
   print('computing perplexity on objective set'      )
   UpperCamelCase       :    List[str]		      =      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 						UpperCamelCase      (  snake_case__			:       List[Any]			,	snake_case__			:       Dict=15			,	snake_case__			:       Tuple=128			,	snake_case__			:       List[str]=100			,	snake_case__			:       Any="igf_model.pt"			,	)      ->	Tuple:
   set_seed(42      )

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

   # Initialize secondary learner to use embedding weights of model
   UpperCamelCase       :    str		      =      SecondaryLearner(UpperCamelCase_      )

   # Train secondary learner
   UpperCamelCase       :    Any		      =      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 						UpperCamelCase      (  snake_case__			:       List[Any]			,	snake_case__			:       Any			,	snake_case__			:       List[str]			,	snake_case__			:       Optional[Any]=32			,	snake_case__			:       List[Any]=1000			,	snake_case__			:       List[Any]=16			,	snake_case__			:       Tuple=1.0			,	snake_case__			:       List[Any]=recopy_gpta			,	snake_case__			:       str=None			,	snake_case__			:       str=10			,	snake_case__			:       Any="gpt2_finetuned.pt"			,	)      ->	int:
   UpperCamelCase       :    List[str]		      =      torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'      )
   UpperCamelCase       :    Optional[Any]		      =      RandomSampler(UpperCamelCase_      )
   UpperCamelCase       :    int		      =      DataLoader(UpperCamelCase_			,	sampler=UpperCamelCase_      )

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

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

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

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

         if secondary_learner is not None:
            UpperCamelCase       :    Union[str, Any]		      =      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:
               UpperCamelCase       :    Optional[int]		      =      -1
            if predicted_q < threshold:
               UpperCamelCase       :    Any		      =      False

            # If we passed the filter, add the context to the batch!
         if do_backprop:
            contexts.append(np.array(context.cpu()      )      )
            UpperCamelCase       :    Dict		      =      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       :    int		      =      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 						UpperCamelCase      (  )      ->	int:
   UpperCamelCase       :    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
   UpperCamelCase       :    Tuple		      =      joblib.load('data/IGF_values.jbl'      )

   # Train secondary learner
   UpperCamelCase       :    Union[str, 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
   UpperCamelCase       :    Optional[Any]		      =      GPTaLMHeadModel.from_pretrained('gpt2'      )
   set_seed(42      )

   # Generate train and test data to train and evaluate gpt2 model
   UpperCamelCase       :    Dict		      =      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()







'''simple docstring'''





class 				UpperCAmelCase			:


 def __init__(      self							:	Union[str, Any] ):
    UpperCAmelCase__	:dict[str, TrieNode]      =      {}  # Mapping from char to TrieNode
    UpperCAmelCase__	:Union[str, Any]      =      False


 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[int]			,       __lowerCamelCase							:	list[str] ):
    for word in words:
       self.insert(__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Dict			,       __lowerCamelCase							:	str ):
    UpperCAmelCase__	:Tuple      =      self
    for char in word:
       if char not in curr.nodes:
          UpperCAmelCase__	:Optional[int]      =      TrieNode()
       UpperCAmelCase__	:Dict      =      curr.nodes[char]
    UpperCAmelCase__	:Optional[int]      =      True


 def        __SCREAMING_SNAKE_CASE					(      self							:	Any			,       __lowerCamelCase							:	str ):
    UpperCAmelCase__	:str      =      self
    for char in word:
       if char not in curr.nodes:
          return False
       UpperCAmelCase__	:Dict      =      curr.nodes[char]
    return curr.is_leaf





 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[Any]			,       __lowerCamelCase							:	str ):

    def _delete(__lowerCamelCase							:	TrieNode			,       __lowerCamelCase							:	str			,       __lowerCamelCase							:	int ) -> bool:
       if index == len(__lowerCamelCase ):
          # If word does not exist
          if not curr.is_leaf:
             return False
          UpperCAmelCase__	:int      =      False
          return len(curr.nodes ) == 0
       UpperCAmelCase__	:str      =      word[index]
       UpperCAmelCase__	:Optional[Any]      =      curr.nodes.get(__lowerCamelCase )
       # If char not in current trie node
       if not char_node:
          return False
       # Flag to check if node can be deleted
       UpperCAmelCase__	:Any      =      _delete(__lowerCamelCase			,       __lowerCamelCase			,       index + 1 )
       if delete_curr:
          del curr.nodes[char]
          return len(curr.nodes ) == 0
       return delete_curr

    _delete(self			,       __lowerCamelCase			,       0 )


def        a__     (					UpperCamelCase_						:      TrieNode,   UpperCamelCase_						:      str						):
   if node.is_leaf:
      print(UpperCamelCase_,   end=''' '''						)

   for key, value in node.nodes.items():
      print_words(UpperCamelCase_,   word + key						)


def        a__     (					):
   UpperCAmelCase__	:Union[str, Any]      =      '''banana bananas bandana band apple all beast'''.split()
   UpperCAmelCase__	:Union[str, Any]      =      TrieNode()
   root.insert_many(UpperCamelCase_						)
   # print_words(root, "")
   assert all(root.find(UpperCamelCase_						) for word in words						)
   assert root.find('''banana'''						)
   assert not root.find('''bandanas'''						)
   assert not root.find('''apps'''						)
   assert root.find('''apple'''						)
   assert root.find('''all'''						)
   root.delete('''all'''						)
   assert not root.find('''all'''						)
   root.delete('''banana'''						)
   assert not root.find('''banana'''						)
   assert root.find('''bananas'''						)
   return True


def        a__     (					UpperCamelCase_						:      str,   UpperCamelCase_						:      bool						):
   print(str(UpperCamelCase_						),   '''works!''' if passes else '''doesn\'t work :('''						)


def        a__     (					):
   assert test_trie()


def        a__     (					):
   print_results('''Testing trie functionality''',   test_trie()						)


if __name__ == "__main__":
    main()



