


'''simple docstring'''



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





     '''simple docstring'''






     set_seed(3						)
     # generate train_data and objective_set
     __UpperCAmelCase			:				str													=    generate_datasets(
         _lowercase    ,      _lowercase    ,      number=_lowercase    ,      min_len=1026    ,      trim=_lowercase						)
     # 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			:				int													=    torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''						)

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

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

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




def 			_a			(		_lowercase      :			int    ,      _lowercase      :			List[str]=15    ,      _lowercase      :			int=128    ,      _lowercase      :			str=100    ,      _lowercase      :			int="igf_model.pt"    ,      ):





     '''simple docstring'''






     set_seed(42						)

     # Load pre-trained model
     __UpperCAmelCase			:				List[str]													=    GPTaLMHeadModel.from_pretrained('''gpt2'''						)

     # Initialize secondary learner to use embedding weights of model
     __UpperCAmelCase			:				Dict													=    SecondaryLearner(_lowercase						)

     # Train secondary learner
     __UpperCAmelCase			:				List[Any]													=    train_secondary_learner(
         _lowercase    ,      _lowercase    ,      max_epochs=_lowercase    ,      batch_size=_lowercase    ,      eval_freq=100    ,      igf_model_path=_lowercase    ,      )

     del model, secondary_learner_train_data
     torch.cuda.empty_cache()

     return secondary_learner




def 			_a			(		_lowercase      :			int    ,      _lowercase      :			Any    ,      _lowercase      :			Optional[int]    ,      _lowercase      :			List[Any]=32    ,      _lowercase      :			str=1000    ,      _lowercase      :			Optional[int]=16    ,      _lowercase      :			Dict=1.0    ,      _lowercase      :			Optional[int]=recopy_gpta    ,      _lowercase      :			Tuple=None    ,      _lowercase      :			Dict=10    ,      _lowercase      :			List[Any]="gpt2_finetuned.pt"    ,      ):





     '''simple docstring'''






     __UpperCAmelCase			:				Optional[Any]													=    torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''						)
     __UpperCAmelCase			:				Dict													=    RandomSampler(_lowercase						)
     __UpperCAmelCase			:				int													=    DataLoader(_lowercase    ,      sampler=_lowercase						)

     __UpperCAmelCase			:				Dict													=    max_steps // (len(_lowercase						)) + 1
     __UpperCAmelCase			:				str													=    0
     __UpperCAmelCase			:				List[str]													=    torch.zeros((1, context_len)    ,      dtype=torch.long    ,      device=_lowercase						)
     __UpperCAmelCase			:				Dict													=    recopy_model(_lowercase    ,      _lowercase    ,      _lowercase						)

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

     __UpperCAmelCase			:				Tuple													=    []
     __UpperCAmelCase			:				Union[str, Any]													=    []

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

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

                    # 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[Any]													=    -1
                    if predicted_q < threshold:
                         __UpperCAmelCase			:				Optional[int]													=    False

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

                         print('''Test perplexity, step'''    ,      _lowercase    ,      ''':'''    ,      _lowercase						)
            # 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()    ,      _lowercase						)
     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'''






     __UpperCAmelCase			:				List[str]													=    argparse.ArgumentParser(description='''Fine-tune a transformer model with IGF on a language modeling task'''						)

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

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

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

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

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

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

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

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

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

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

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

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

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

     # Load train data for secondary learner
     __UpperCAmelCase			:				Optional[int]													=    joblib.load('''data/IGF_values.jbl'''						)

     # Train secondary learner
     __UpperCAmelCase			:				str													=    training_secondary_learner(
         _lowercase    ,      secondary_learner_max_epochs=15    ,      secondary_learner_batch_size=128    ,      eval_freq=100    ,      igf_model_path='''igf_model.pt'''    ,      )

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

     # Generate train and test data to train and evaluate gpt2 model
     __UpperCAmelCase			:				int													=    generate_datasets(
         context_len=32    ,      file='''data/tokenized_stories_train_wikitext103.jbl'''    ,      number=100    ,      min_len=1026    ,      trim=_lowercase						)

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


if __name__ == "__main__":
      main()



'''simple docstring'''



from collections.abc import Iterable
from typing import Any



class 			a       :


     """simple docstring"""






     def __init__(       self  :	Any							,				snake_case  :	int | None = None    )		->							int:
          __UpperCAmelCase			:				str													=    value
          __UpperCAmelCase			:				Node | None													=    None  # Added in order to delete a node easier
          __UpperCAmelCase			:				Node | None													=    None
          __UpperCAmelCase			:				Node | None													=    None






     def __repr__(       self  :	str    )		->							str:
          from pprint import pformat

          if self.left is None and self.right is None:
               return str(self.value    )
          return pformat({f'{self.value}': (self.left, self.right)}							,				indent=1    )



class 			a       :


     """simple docstring"""






     def __init__(       self  :	Optional[int]							,				snake_case  :	Node | None = None    )		->							str:
          __UpperCAmelCase			:				Optional[Any]													=    root

     def __str__(       self  :	str    )		->							str:
          return str(self.root    )

     def 				lowerCamelCase__				(       self  :	Union[str, Any]							,				snake_case  :	Node							,				snake_case  :	Node | None    )		->							None:
          if new_children is not None:  # reset its kids
               __UpperCAmelCase			:				List[str]													=    node.parent
          if node.parent is not None:  # reset its parent
               if self.is_right(snake_case    ):  # If it is the right children
                    __UpperCAmelCase			:				int													=    new_children
               else:
                    __UpperCAmelCase			:				Tuple													=    new_children
          else:
               __UpperCAmelCase			:				List[Any]													=    new_children

     def 				lowerCamelCase__				(       self  :	Optional[int]							,				snake_case  :	Node    )		->							bool:
          if node.parent and node.parent.right:
               return node == node.parent.right
          return False

     def 				lowerCamelCase__				(       self  :	int    )		->							bool:
          return self.root is None

     def 				lowerCamelCase__				(       self  :	Optional[int]							,				snake_case  :	Optional[Any]    )		->							None:
          __UpperCAmelCase			:				int													=    Node(snake_case    )  # create a new Node
          if self.empty():  # if Tree is empty
               __UpperCAmelCase			:				List[Any]													=    new_node  # set its root
          else:  # Tree is not empty
               __UpperCAmelCase			:				Tuple													=    self.root  # from root
               if parent_node is None:
                    return
               while True:  # While we don't get to a leaf
                    if value < parent_node.value:  # We go left
                         if parent_node.left is None:
                              __UpperCAmelCase			:				Optional[int]													=    new_node  # We insert the new node in a leaf
                              break
                         else:
                              __UpperCAmelCase			:				List[Any]													=    parent_node.left
                    else:
                         if parent_node.right is None:
                              __UpperCAmelCase			:				Optional[int]													=    new_node
                              break
                         else:
                              __UpperCAmelCase			:				List[str]													=    parent_node.right
               __UpperCAmelCase			:				int													=    parent_node

     def 				lowerCamelCase__				(       self  :	Optional[int]							,				*snake_case  :	List[Any]    )		->							None:
          for value in values:
               self.__insert(snake_case    )

     def 				lowerCamelCase__				(       self  :	Optional[int]							,				snake_case  :	Dict    )		->							Node | None:
          if self.empty():
               raise IndexError('''Warning: Tree is empty! please use another.'''    )
          else:
               __UpperCAmelCase			:				List[Any]													=    self.root
               # use lazy evaluation here to avoid NoneType Attribute error
               while node is not None and node.value is not value:
                    __UpperCAmelCase			:				Union[str, Any]													=    node.left if value < node.value else node.right
               return node

     def 				lowerCamelCase__				(       self  :	str							,				snake_case  :	Node | None = None    )		->							Node | None:
          if node is None:
               if self.root is None:
                    return None
               __UpperCAmelCase			:				Optional[Any]													=    self.root

          if not self.empty():
               while node.right is not None:
                    __UpperCAmelCase			:				str													=    node.right
          return node

     def 				lowerCamelCase__				(       self  :	int							,				snake_case  :	Node | None = None    )		->							Node | None:
          if node is None:
               __UpperCAmelCase			:				str													=    self.root
          if self.root is None:
               return None
          if not self.empty():
               __UpperCAmelCase			:				List[str]													=    self.root
               while node.left is not None:
                    __UpperCAmelCase			:				str													=    node.left
          return node

     def 				lowerCamelCase__				(       self  :	Union[str, Any]							,				snake_case  :	int    )		->							None:
          __UpperCAmelCase			:				List[str]													=    self.search(snake_case    )  # Look for the node with that label
          if node is not None:
               if node.left is None and node.right is None:  # If it has no children
                    self.__reassign_nodes(snake_case							,				snake_case    )
               elif node.left is None:  # Has only right children
                    self.__reassign_nodes(snake_case							,				node.right    )
               elif node.right is None:  # Has only left children
                    self.__reassign_nodes(snake_case							,				node.left    )
               else:
                    __UpperCAmelCase			:				Optional[int]													=    self.get_max(
                        node.left    )  # Gets the max value of the left branch
                    self.remove(tmp_node.value    )  # type: ignore
                    __UpperCAmelCase			:				List[str]													=    (
                        tmp_node.value  # type: ignore
                    )  # Assigns the value to the node to delete and keep tree structure

     def 				lowerCamelCase__				(       self  :	List[str]							,				snake_case  :	Node | None    )		->							Iterable:
          if node is not None:
               yield node  # Preorder Traversal
               yield from self.preorder_traverse(node.left    )
               yield from self.preorder_traverse(node.right    )

     def 				lowerCamelCase__				(       self  :	Union[str, Any]							,				snake_case  :	int=None    )		->							Any:
          if traversal_function is None:
               return self.preorder_traverse(self.root    )
          else:
               return traversal_function(self.root    )

     def 				lowerCamelCase__				(       self  :	str							,				snake_case  :	list							,				snake_case  :	Node | None    )		->							None:
          if node:
               self.inorder(snake_case							,				node.left    )
               arr.append(node.value    )
               self.inorder(snake_case							,				node.right    )






     def 				lowerCamelCase__				(       self  :	Optional[int]							,				snake_case  :	int							,				snake_case  :	Node    )		->							int:
          __UpperCAmelCase			:				list[int]													=    []
          self.inorder(snake_case							,				snake_case    )  # append all values to list using inorder traversal
          return arr[k - 1]




def 			_a			(		_lowercase      :			Node | None						):





     '''simple docstring'''






     __UpperCAmelCase			:				List[str]													=    []
     if curr_node is not None:
          __UpperCAmelCase			:				Union[str, Any]													=    postorder(curr_node.left						) + postorder(curr_node.right						) + [curr_node]
     return node_list




def 			_a			(		):





     '''simple docstring'''






     __UpperCAmelCase			:				Optional[Any]													=    (8, 3, 6, 1, 10, 14, 13, 4, 7)
     __UpperCAmelCase			:				Union[str, Any]													=    BinarySearchTree()
     for i in testlist:
          t.insert(_lowercase						)

     # Prints all the elements of the list in order traversal
     print(_lowercase						)

     if t.search(6						) is not None:
          print('''The value 6 exists'''						)
     else:
          print('''The value 6 doesn\'t exist'''						)

     if t.search(-1						) is not None:
          print('''The value -1 exists'''						)
     else:
          print('''The value -1 doesn\'t exist'''						)

     if not t.empty():
          print('''Max Value: '''    ,      t.get_max().value						)  # type: ignore
          print('''Min Value: '''    ,      t.get_min().value						)  # type: ignore

     for i in testlist:
          t.remove(_lowercase						)
          print(_lowercase						)


if __name__ == "__main__":
      import doctest

      doctest.testmod(verbose=True)