
'''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    UpperCAmelCase_						(__a :       int=3_2					,      __a :       Dict=1_0					,      __a :       Optional[Any]=1_0_0					,      __a :       List[str]=1_0_2_6					,      __a :       Tuple=True					,      __a :       str="data/tokenized_stories_train_wikitext103.jbl"					,      __a :       List[Any]="igf_context_pairs.jbl"					,      ):






   """simple docstring"""

   set_seed(3					)
   # generate train_data and objective_set
   _a							:      str     =  generate_datasets(
       _snake_case					,      _snake_case					,      number=_snake_case					,      min_len=1_0_2_6					,      trim=_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?
   _a							:      List[Any]     =  torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'					)

   # load pretrained model
   _a							:      Tuple     =  load_gpta('gpt2'					).to(_snake_case					)
   print('computing perplexity on objective set'					)
   _a							:      Any     =  compute_perplexity(_snake_case					,      _snake_case					,      _snake_case					).item()
   print('perplexity on objective set:'					,      _snake_case					)

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

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





def    UpperCAmelCase_						(__a :       Tuple					,      __a :       Optional[int]=1_5					,      __a :       Optional[Any]=1_2_8					,      __a :       Any=1_0_0					,      __a :       int="igf_model.pt"					,      ):






   """simple docstring"""

   set_seed(4_2					)

   # Load pre-trained model
   _a							:      str     =  GPTaLMHeadModel.from_pretrained('gpt2'					)

   # Initialize secondary learner to use embedding weights of model
   _a							:      List[Any]     =  SecondaryLearner(_snake_case					)

   # Train secondary learner
   _a							:      Tuple     =  train_secondary_learner(
       _snake_case					,      _snake_case					,      max_epochs=_snake_case					,      batch_size=_snake_case					,      eval_freq=1_0_0					,      igf_model_path=_snake_case					,      )

   del model, secondary_learner_train_data
   torch.cuda.empty_cache()

   return secondary_learner





def    UpperCAmelCase_						(__a :       List[Any]					,      __a :       Optional[Any]					,      __a :       int					,      __a :       Dict=3_2					,      __a :       Any=1_0_0_0					,      __a :       Any=1_6					,      __a :       List[str]=1.0					,      __a :       int=recopy_gpta					,      __a :       Tuple=None					,      __a :       List[Any]=1_0					,      __a :       Optional[Any]="gpt2_finetuned.pt"					,      ):






   """simple docstring"""

   _a							:      Tuple     =  torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'					)
   _a							:      int     =  RandomSampler(_snake_case					)
   _a							:      List[Any]     =  DataLoader(_snake_case					,      sampler=_snake_case					)

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

   model.train()
   if secondary_learner is not None:
      secondary_learner.to(_snake_case					)
      secondary_learner.eval()
   _a							:      Optional[Any]     =  []
   _a							:      int     =  0

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

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

         if secondary_learner is not None:
            _a							:      List[str]     =  secondary_learner.forward(
                torch.tensor(_snake_case					,      dtype=torch.long					,      device=_snake_case					).unsqueeze(0					)					)[0].item()
            observed_qs.append(float(_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 == 1_0:
               _a							:      List[Any]     =  -1
            if predicted_q < threshold:
               _a							:      Optional[int]     =  False

            # If we passed the filter, add the context to the batch!
         if do_backprop:
            contexts.append(np.array(context.cpu()					)					)
            _a							:      List[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()
            _a							:      Optional[int]     =  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							:      List[Any]     =  compute_perplexity(_snake_case					,      _snake_case					,      _snake_case					)
               test_perps.append(_snake_case					)

               print('Test perplexity, step'					,      _snake_case					,      ':'					,      _snake_case					)
            # Break out of the loop after 60 batches
         if max_steps > 0 and global_step > 6_0:
            break
      if max_steps > 0 and global_step > 6_0:
         break

    # save finetuned transformer model
   torch.save(model.state_dict()					,      _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    UpperCAmelCase_						():






   """simple docstring"""

   _a							:      Tuple     =  argparse.ArgumentParser(description='Fine-tune a transformer model with IGF on a language modeling task'					)

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

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

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

   parser.add_argument(
       '--context_len'					,      default=3_2					,      type=_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_0_0					,      type=_snake_case					,      help='number of articles that are long enough to be used as our objective set'					,      )
   parser.add_argument(
       '--eval_freq'					,      default=1_0_0					,      type=_snake_case					,      help='secondary model evaluation is triggered at eval_freq'					)

   parser.add_argument('--max_steps'					,      default=1_0_0_0					,      type=_snake_case					,      help='To calculate training epochs'					)

   parser.add_argument(
       '--secondary_learner_batch_size'					,      default=1_2_8					,      type=_snake_case					,      help='batch size of training data for secondary learner'					,      )

   parser.add_argument(
       '--batch_size'					,      default=1_6					,      type=_snake_case					,      help='batch size of training data of language model(gpt2) '					)

   parser.add_argument(
       '--eval_interval'					,      default=1_0					,      type=_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_0_0					,      type=_snake_case					,      help='The number of examples split to be used as objective_set/test_data'					)

   parser.add_argument(
       '--min_len'					,      default=1_0_2_6					,      type=_snake_case					,      help='The minimum length of the article to be used as objective set'					)

   parser.add_argument(
       '--secondary_learner_max_epochs'					,      default=1_5					,      type=_snake_case					,      help='number of epochs to train secondary learner'					)

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

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

   parser.add_argument(
       '--recopy_model'					,      default=_snake_case					,      type=_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=3_2					,      max_steps=1_0					,      size_objective_set=1_0_0					,      min_len=1_0_2_6					,      trim=_snake_case					,      data_file='data/tokenized_stories_train_wikitext103.jbl'					,      igf_data_file='igf_context_pairs.jbl'					,      )

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

   # Train secondary learner
   _a							:      str     =  training_secondary_learner(
       _snake_case					,      secondary_learner_max_epochs=1_5					,      secondary_learner_batch_size=1_2_8					,      eval_freq=1_0_0					,      igf_model_path='igf_model.pt'					,      )

   # load pretrained gpt2 model
   _a							:      Optional[int]     =  GPTaLMHeadModel.from_pretrained('gpt2'					)
   set_seed(4_2					)

   # Generate train and test data to train and evaluate gpt2 model
   _a							:      str     =  generate_datasets(
       context_len=3_2					,      file='data/tokenized_stories_train_wikitext103.jbl'					,      number=1_0_0					,      min_len=1_0_2_6					,      trim=_snake_case					)

   # fine-tuning of the gpt2 model using igf (Information Gain Filtration)
   finetune(
       _snake_case					,      _snake_case					,      _snake_case					,      context_len=3_2					,      max_steps=1_0_0_0					,      batch_size=1_6					,      threshold=1.0					,      recopy_model=_snake_case					,      secondary_learner=_snake_case					,      eval_interval=1_0					,      finetuned_model_name='gpt2_finetuned.pt'					,      )


if __name__ == "__main__":
  main()





"""simple docstring"""



from __future__ import annotations

import queue





class 				a			:



      def __init__(   self     ,	UpperCamelCase_      ):
          UpperCAmelCase__	:      int     							=					data
          UpperCAmelCase__	:      Dict     							=					None
          UpperCAmelCase__	:      Optional[int]     							=					None




def 					lowerCamelCase   (     ):
    print('\n********Press N to stop entering at any point of time********\n' )
    UpperCAmelCase__	:      int     							=					input('Enter the value of the root node: ' ).strip().lower()
    UpperCAmelCase__	:      queue.Queue     							=					queue.Queue()
    UpperCAmelCase__	:      Dict     							=					TreeNode(int(_snake_case ) )
    q.put(_snake_case )
    while not q.empty():
        UpperCAmelCase__	:      Tuple     							=					q.get()
        UpperCAmelCase__	:      List[Any]     							=					F'''Enter the left node of {node_found.data}: '''
        UpperCAmelCase__	:      Dict     							=					input(_snake_case ).strip().lower() or 'n'
        if check == "n":
            return tree_node
        UpperCAmelCase__	:      Optional[int]     							=					TreeNode(int(_snake_case ) )
        UpperCAmelCase__	:      Tuple     							=					left_node
        q.put(_snake_case )
        UpperCAmelCase__	:      int     							=					F'''Enter the right node of {node_found.data}: '''
        UpperCAmelCase__	:      int     							=					input(_snake_case ).strip().lower() or 'n'
        if check == "n":
            return tree_node
        UpperCAmelCase__	:      int     							=					TreeNode(int(_snake_case ) )
        UpperCAmelCase__	:      Any     							=					right_node
        q.put(_snake_case )
    raise
def 					lowerCamelCase   (     _snake_case ):
    if not isinstance(_snake_case    ,_snake_case ) or not node:
        return
    print(node.data    ,end=',' )
    pre_order(node.left )
    pre_order(node.right )
def 					lowerCamelCase   (     _snake_case ):
    if not isinstance(_snake_case    ,_snake_case ) or not node:
        return
    in_order(node.left )
    print(node.data    ,end=',' )
    in_order(node.right )
def 					lowerCamelCase   (     _snake_case ):
    if not isinstance(_snake_case    ,_snake_case ) or not node:
        return
    post_order(node.left )
    post_order(node.right )
    print(node.data    ,end=',' )
def 					lowerCamelCase   (     _snake_case ):
    if not isinstance(_snake_case    ,_snake_case ) or not node:
        return
    UpperCAmelCase__	:      queue.Queue     							=					queue.Queue()
    q.put(_snake_case )
    while not q.empty():
        UpperCAmelCase__	:      int     							=					q.get()
        print(node_dequeued.data    ,end=',' )
        if node_dequeued.left:
            q.put(node_dequeued.left )
        if node_dequeued.right:
            q.put(node_dequeued.right )
def 					lowerCamelCase   (     _snake_case ):
    if not isinstance(_snake_case    ,_snake_case ) or not node:
        return
    UpperCAmelCase__	:      queue.Queue     							=					queue.Queue()
    q.put(_snake_case )
    while not q.empty():
        UpperCAmelCase__	:      Optional[Any]     							=					[]
        while not q.empty():
            UpperCAmelCase__	:      Union[str, Any]     							=					q.get()
            print(node_dequeued.data    ,end=',' )
            if node_dequeued.left:
                list_.append(node_dequeued.left )
            if node_dequeued.right:
                list_.append(node_dequeued.right )
        print()
        for node in list_:
            q.put(_snake_case )
def 					lowerCamelCase   (     _snake_case ):
    if not isinstance(_snake_case    ,_snake_case ) or not node:
        return
    UpperCAmelCase__	:      list[TreeNode]     							=					[]
    UpperCAmelCase__	:      Optional[int]     							=					node
    while n or stack:
        while n:  # start from root node, find its left child
            print(n.data    ,end=',' )
            stack.append(_snake_case )
            UpperCAmelCase__	:      Optional[int]     							=					n.left
        # end of while means current node doesn't have left child
        UpperCAmelCase__	:      Union[str, Any]     							=					stack.pop()
        # start to traverse its right child
        UpperCAmelCase__	:      Dict     							=					n.right
def 					lowerCamelCase   (     _snake_case ):
    if not isinstance(_snake_case    ,_snake_case ) or not node:
        return
    UpperCAmelCase__	:      list[TreeNode]     							=					[]
    UpperCAmelCase__	:      Optional[Any]     							=					node
    while n or stack:
        while n:
            stack.append(_snake_case )
            UpperCAmelCase__	:      int     							=					n.left
        UpperCAmelCase__	:      Tuple     							=					stack.pop()
        print(n.data    ,end=',' )
        UpperCAmelCase__	:      Tuple     							=					n.right
def 					lowerCamelCase   (     _snake_case ):
    if not isinstance(_snake_case    ,_snake_case ) or not node:
        return
    UpperCAmelCase__							,							UpperCAmelCase__	:      str     							=					[], []
    UpperCAmelCase__	:      Union[str, Any]     							=					node
    stacka.append(_snake_case )
    while stacka:  # to find the reversed order of post order, store it in stack2
        UpperCAmelCase__	:      Tuple     							=					stacka.pop()
        if n.left:
            stacka.append(n.left )
        if n.right:
            stacka.append(n.right )
        stacka.append(_snake_case )
    while stacka:  # pop up from stack2 will be the post order
        print(stacka.pop().data    ,end=',' )
def 					lowerCamelCase   (     _snake_case = ""    ,_snake_case=50    ,_snake_case="*" ):
    if not s:
        return "\n" + width * char
    UpperCAmelCase__							,							UpperCAmelCase__	:      str     							=					divmod(width - len(_snake_case ) - 2    ,2 )
    return F'''{left * char} {s} {(left + extra) * char}'''


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    print(prompt('Binary Tree Traversals'))

    UpperCamelCase__          =	build_tree()
    print(prompt('Pre Order Traversal'))
    pre_order(node)
    print(prompt() + '\n')

    print(prompt('In Order Traversal'))
    in_order(node)
    print(prompt() + '\n')

    print(prompt('Post Order Traversal'))
    post_order(node)
    print(prompt() + '\n')

    print(prompt('Level Order Traversal'))
    level_order(node)
    print(prompt() + '\n')

    print(prompt('Actual Level Order Traversal'))
    level_order_actual(node)
    print('*' * 50 + '\n')

    print(prompt('Pre Order Traversal - Iteration Version'))
    pre_order_iter(node)
    print(prompt() + '\n')

    print(prompt('In Order Traversal - Iteration Version'))
    in_order_iter(node)
    print(prompt() + '\n')

    print(prompt('Post Order Traversal - Iteration Version'))
    post_order_iter(node)
    print(prompt())

